Supplementary Report S2. Report with ASV without OTU post-clustering. Terroir and farming practices drive arbuscular mycorrhizal fungal communities in French vineyard.

Code

library(targets)
library(MiscMetabar)
library(metacoder)
library(DT)
library(knitr)
library(ggplot2)
library(patchwork)
library(vegan)
library(FactoMineR)
library(factoextra)
library(DESeq2)
library(indicspecies)
library(sf)
library(adespatial)
library(ade4)
library(adegraphics)
library(kableExtra)
library(formattable)
library(microbiomeMarker)
library(ComplexUpset)
library(tidyverse)
library(gt)

library(conflicted)
conflicts_prefer(dplyr::filter)
conflicts_prefer(dplyr::rename)
conflicts_prefer(base::setdiff)
conflicts_prefer(dplyr::lag)

Code

d_asv_blast <- tar_read(d_blast)
d_asv <- tar_read(data_phyloseq_PR2)

sam_data_updated <- read.delim("data/samples_data/sam_data_english.csv")
rownames(sam_data_updated) <- sam_data_updated[,1]
d_asv_blast <- update_sam_data(d_asv_blast, sam_data_updated, overwrite = TRUE)
d_asv <- update_sam_data(d_asv, sam_data_updated, overwrite = TRUE)

Code

d <- d_asv_blast
d <- clean_pq(d, verbose = TRUE, force_taxa_as_columns = TRUE)

Taxa are now in columns.

Cleaning suppress 0 taxa (  ) and 0 sample(s) (  ).

Code

taxa_names(d) <- paste0("OTU_", seq_along(taxa_names(d)))

Metadata modification

Spores number

Code

d@sam_data$nb_spores <- rowMeans(cbind(as.numeric(d@sam_data$spores_rep1), as.numeric(d@sam_data$spores_rep2),as.numeric(d@sam_data$spores_rep3)),na.rm=TRUE)

Practices

Code

d@sam_data$organic <- ifelse(!d@sam_data$practice %in%
                               c("Conventional", "Conversion"),
                             "Organic", "NonOrganic"
)

Paired samples (roots, spores from the same samples)

Code

d@sam_data$paired_name <-
  apply(as.matrix(d@sam_data[, c(5:16)]), 1, paste,
        collapse = "--"
  )
d@sam_data$paired_name[d@sam_data$terroir == "Vergèze"] <-
  gsub("R", "", d@sam_data$ref_mycea[d@sam_data$terroir == "Vergèze"])

Code

tib_sam_data <- as_tibble(d@sam_data) %>%
  mutate(across(starts_with("Myc_freq"), as.numeric)) %>%
  mutate(across(starts_with("Myc_intensity_colonization"), as.numeric)) %>%
  mutate(across(starts_with("Myc_intensity_root"), as.numeric)) %>%
  mutate(across(starts_with("Arbuscul_richness"), as.numeric)) %>%
  mutate(across(starts_with("Arbuscul_abundance"), as.numeric)) %>%
  mutate(across(starts_with("Vesicle_richness"), as.numeric)) %>%
  mutate(across(starts_with("Vesicle_abundance"), as.numeric)) %>%
  mutate(Myc_freq = rowMeans(select(., starts_with("Myc_freq")), na.rm = TRUE)) %>%
  mutate(Myc_intensity_colonization = rowMeans(select(
    ., starts_with("Myc_intensity_colonization")
  ), na.rm = TRUE)) %>%
  mutate(Myc_intensity_root = rowMeans(select(., starts_with(
    "Myc_intensity_root"
  )), na.rm = TRUE)) %>%
  mutate(Arbuscul_richness = rowMeans(select(., starts_with(
    "Arbuscul_richness"
  )), na.rm = TRUE)) %>%
  mutate(Arbuscul_abundance = rowMeans(select(., starts_with(
    "Arbuscul_abundance"
  )), na.rm = TRUE)) %>%
  mutate(Vesicle_richness = rowMeans(
    select(., starts_with(
      "Vesicle_richness"
    )),
    na.rm = TRUE
  )) %>%
  mutate(Vesicle_abundance = rowMeans(
    select(., starts_with(
      "Vesicle_abundance"
    )),
    na.rm = TRUE
  )) %>% tibble::column_to_rownames(var = "X")

sample_data(d) <- tib_sam_data

Exclude Mycelium

Code

d <- clean_pq(subset_samples(d, compartment != "Mycelium"))

Cleaning suppress 117 taxa and 0 samples.

Code

d_asv <- clean_pq(subset_samples(d_asv, compartment != "Mycelium"))

Cleaning suppress 1723 taxa and 0 samples.

Verify number of sequences per samples

Code

hist(log10(sample_sums(d_asv)), breaks = 40)

Number of sequences per samples (all Phyla)

Code

summary(sample_sums(d_asv))

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   2856   26872   36324   40116   48250  112831

Code

sd(sample_sums(d_asv))

[1] 19487.06

The samples with the lower nb of total sequences is 21MET828 (2856 seq.) but it contains only AMF sequences. So we keep all samples.

Code

hist(log10(sample_sums(d)), breaks = 40)

Number of sequences per samples (AMF only)

Code

summary(sample_sums(d))

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    837   12329   21866   25190   32704   91395

Code

sd(sample_sums(d))

[1] 17966.48

Summary of final dataset

Code

summary_plot_pq(d) +
  labs(title = "Summary of final dataset") +
  theme(plot.title = element_text(hjust = 0.5, size = 20, color = "#1e2b4c"))

Cleaning suppress 0 taxa and 0 samples.

Summary of bioinformatics pipeline

Global summary

Code

kable(tar_read(track_sequences_samples_clusters))

	nb_sequences	nb_clusters	nb_samples
Paired sequences	8283717	28637	194
Paired sequences without chimera	7784063	10298	194
Paired sequences without chimera and longer than 200bp	7737954	7815	194
ASV denoising	7534699	7815	186
OTU after vsearch reclustering at 97%	7534699	1182	186
OTU after blast filter without reclustering	3821515	3916	182
OTU after blast filter with reclustering	3823905	239	182

# TAXONOMICAL ANALYSES

Code

# Fig S9
multitax_bar_pq(d_asv, "Supergroup_PR2",
                "Subdivision_PR2", "Class_PR2",
                nb_seq = TRUE
) +
  ggtitle("Number of sequences (log10) including non-Mucoromycota")

Code

multitax_bar_pq(d_asv, "Supergroup_PR2",
                "Subdivision_PR2", "Class_PR2",
                nb_seq = FALSE,
                log10trans = FALSE
) +
  ggtitle("Number of OTUs including non-Mucoromycota")

Code

# VALUE
seq_mucoromycota <-
  sum(taxa_sums(d_asv@otu_table)[as.vector(d_asv@tax_table[, "Class_PR2"] ==
                                            "Mucoromycota")], na.rm = T)
asv_mucoromycota <-
  sum(taxa_sums(d_asv@otu_table)[as.vector(d_asv@tax_table[, "Class_PR2"] ==
                                            "Mucoromycota")] > 0, na.rm = T)

seq_arthropoda <-
  sum(taxa_sums(d_asv@otu_table)[as.vector(d_asv@tax_table[, "Class_PR2"] ==
                                            "Arthropoda")], na.rm = T)
asv_arthropoda <-
  sum(taxa_sums(d_asv@otu_table)[as.vector(d_asv@tax_table[, "Class_PR2"] ==
                                            "Arthropoda")] > 0, na.rm = T)

seq_chelicerata <-
  sum(taxa_sums(d_asv@otu_table)[as.vector(d_asv@tax_table[, "Order_PR2"] ==
                                            "Chelicerata")], na.rm = T)
asv_chelicerata <-
  sum(taxa_sums(d_asv@otu_table)[as.vector(d_asv@tax_table[, "Order_PR2"] ==
                                            "Chelicerata")] > 0, na.rm = T)

seq_nematoda <-
  sum(taxa_sums(d_asv@otu_table)[as.vector(d_asv@tax_table[, "Class_PR2"] ==
                                            "Nematoda")], na.rm = T)
asv_nematoda <-
  sum(taxa_sums(d_asv@otu_table)[as.vector(d_asv@tax_table[, "Class_PR2"] ==
                                            "Nematoda")] > 0, na.rm = T)

seq_annelida <-
  sum(taxa_sums(d_asv@otu_table)[as.vector(d_asv@tax_table[, "Class_PR2"] ==
                                            "Annelida")], na.rm = T)
asv_annelida <-
  sum(taxa_sums(d_asv@otu_table)[as.vector(d_asv@tax_table[, "Class_PR2"] ==
                                            "Annelida")] > 0, na.rm = T)

seq_tardigrada <-
  sum(taxa_sums(d_asv@otu_table)[as.vector(d_asv@tax_table[, "Class_PR2"] ==
                                            "Tardigrada")], na.rm = T)
asv_tardigrada <-
  sum(taxa_sums(d_asv@otu_table)[as.vector(d_asv@tax_table[, "Class_PR2"] ==
                                            "Tardigrada")] > 0, na.rm = T)

df <- data.frame(
  "Class" = c(
    "All Taxa",
    "Mucoromycota",
    "Arthropoda",
    "including Chelicerata Order",
    "Nematoda",
    "Annelida",
    "Tardigrada"
  ),
  "nb_seq" = c(
    sum(d_asv@otu_table),
    seq_mucoromycota,
    seq_arthropoda,
    seq_chelicerata,
    seq_nematoda,
    seq_annelida,
    seq_tardigrada
  ),
  "nb_ASV" = c(
    ntaxa(d_asv),
    asv_mucoromycota,
    asv_arthropoda,
    asv_chelicerata,
    asv_nematoda,
    asv_annelida,
    asv_tardigrada
  )
)

kable(df)

Class	nb_seq	nb_ASV
All Taxa	5937106	5979
Mucoromycota	3622113	3696
Arthropoda	1764623	1548
including Chelicerata Order	947550	999
Nematoda	167513	195
Annelida	45409	46
Tardigrada	133628	117

Code

d_muco <- clean_pq(subset_taxa(d, Class_PR2 == "Mucoromycota"))

Cleaning suppress 0 taxa and 0 samples.

Code

d_asv_blast <- clean_pq(subset_taxa(d_asv_blast, Class_PR2 == "Mucoromycota"))

Cleaning suppress 0 taxa and 3 samples.

Code

df <- data.frame(
  "Proportion of sequences" = c(
    round(
      sum(d_asv_blast@otu_table) / sum(d_asv@otu_table), 3
    ),
    round(
      sum(d_muco@otu_table) / sum(d_asv@otu_table), 3
    )
  ),
  "Proportion of OTU" = c(
    round(
      ntaxa(d_asv_blast) / ntaxa(d_asv), 3
    ),
    round(
      ntaxa(d_muco) / ntaxa(d_asv), 3
    )
  )
)

rownames(df) <- c(
  "Blast filter on Maarjam",
  "Add a filter on Mucoromycota family in PR2"
)
kable(df)

	Proportion.of.sequences	Proportion.of.OTU
Blast filter on Maarjam	0.625	0.627
Add a filter on Mucoromycota family in PR2	0.610	0.618

SUBSETING AND FILTERING DATASET

Code

for(i in c(19:46)){
  d_muco@sam_data[,i] <- as.numeric(unlist(d_muco@sam_data[,i]))
}

Code

d_roots <- clean_pq(subset_samples(d_muco, compartment == "Roots"))

Cleaning suppress 1004 taxa and 0 samples.

Code

d_spores <- clean_pq(subset_samples(d_muco, compartment == "Spores"))

Cleaning suppress 2543 taxa and 0 samples.

Code

d_rs_merged_samples <- clean_pq(merge_samples2(
  d_muco,
  "paired_name",
  default_fun = 
    function(x){MiscMetabar::diff_fct_diff_class(x, na.rm=T)}
))

Cleaning suppress 0 taxa and 0 samples.

Code

d_rs <- clean_pq(subset_samples_pq(
  d_rs_merged_samples,
  sample_sums(d_rs_merged_samples) > 2000
))

Cleaning suppress 0 taxa and 0 samples.

Code

# VALUE
(sort(sample_sums(d_rs_merged_samples)))[1]

PE13 
6768

After merging soil and root tips paired samples, the minimum nb of sequences per samples is 6768

Code

spores_roots_samples <- sample_names(d_asv_blast)[d_asv_blast@sam_data$compartment!="Mycelium"]

tar_load(derep_fs)
tar_load(seq_tab_Pairs)
tar_load(seqtab_wo_chimera)
tar_load(seqtab)
tar_load(data_phyloseq_PR2)

# TABLE XXX_pipeline
tw <- track_wkflow(
  list(
    "Raw data" = tar_read(data_raw)$fnfs[grepl(paste(spores_roots_samples, collapse='|'), tar_read(data_raw)$fnfs)],
    "Quality-filtered data" = derep_fs[grepl(paste(spores_roots_samples, collapse='|'),names(derep_fs))],
    "Paired denoised sequences (ASV)" = seq_tab_Pairs[grepl(paste(spores_roots_samples, collapse='|'),rownames(seq_tab_Pairs)),colSums(seq_tab_Pairs[grepl(paste(spores_roots_samples, collapse='|'),rownames(seq_tab_Pairs)),])>0],
    # "ASV without chimera" = seqtab_wo_chimera[grepl(paste(spores_roots_samples, collapse='|'),rownames(seqtab_wo_chimera)),],
    "ASV without chimera and longer than 200bp" = seqtab[grepl(paste(spores_roots_samples, collapse='|'),rownames(seqtab)),colSums(seqtab[grepl(paste(spores_roots_samples, collapse='|'),rownames(seqtab)),])>0],
    "OTU after vsearch reclustering at 97%" =clean_pq(subset_samples(tar_read(data_phyloseq_PR2), compartment != "Mycelium")),
    "Maarjam Filtered" =   clean_pq(subset_samples(tar_read(d_blast), compartment != "Mycelium")),
    "PR2 Filtered" = d_muco,
    "Roots only" = d_roots,
    "Spores only" = d_spores,
    "Roots + spores merged" = d_rs_merged_samples
  )
)

Compute the number of sequences

Cleaning suppress 1836 taxa and 0 samples.

Cleaning suppress 117 taxa and 0 samples.

Start object of class: character

Start object of class: list

Start object of class: matrixStart object of class: array
Start object of class: matrixStart object of class: array

Start object of class: phyloseq
Start object of class: phyloseq
Start object of class: phyloseq
Start object of class: phyloseq
Start object of class: phyloseq
Start object of class: phyloseq

Compute the number of clusters

Start object of class: character

Start object of class: list

Start object of class: matrixStart object of class: array
Start object of class: matrixStart object of class: array

Start object of class: phyloseq
Start object of class: phyloseq
Start object of class: phyloseq
Start object of class: phyloseq
Start object of class: phyloseq
Start object of class: phyloseq

Compute the number of samples

Start object of class: character

Start object of class: list

Start object of class: matrixStart object of class: array
Start object of class: matrixStart object of class: array

Start object of class: phyloseq
Start object of class: phyloseq
Start object of class: phyloseq
Start object of class: phyloseq
Start object of class: phyloseq
Start object of class: phyloseq

Code

colnames(tw) <- c("Sequences", "Clusters", "Samples")

tw <- tw %>% mutate("diff_seq"=Sequences-lag(Sequences)) %>% mutate("diff_cluster"=Clusters-lag(Clusters))

colnames(tw) <- c("Sequences", "Clusters", "Samples", "Diff sequences", "Diff cluster")
kable(tw)

	Sequences	Clusters	Samples	Diff sequences	Diff cluster
Raw data	9467622	NA	148	NA	NA
Quality-filtered data	9464150	3575734	148	-3472	NA
Paired denoised sequences (ASV)	6278857	18758	148	-3185293	-3556976
ASV without chimera and longer than 200bp	5937106	5979	148	-341751	-12779
OTU after vsearch reclustering at 97%	5937106	5979	148	0	0
Maarjam Filtered	3728170	3799	148	-2208936	-2180
PR2 Filtered	3622090	3695	148	-106080	-104
Roots only	2595746	2691	73	-1026344	-1004
Spores only	1026344	1152	75	-1569402	-1539
Roots + spores merged	3622090	3695	75	2595746	2543

Code

summary_plot_pq(clean_pq(subset_samples(subset_taxa(tar_read(d_blast), Class_PR2 == "Mucoromycota"), compartment != "Mycelium")))

Cleaning suppress 56 taxa and 0 samples.

Cleaning suppress 0 taxa and 0 samples.

Zoom on the filtering processes

Number of sequences

Code

# Fig S7
dir.create("output/krona")

Warning in dir.create("output/krona"): 'output/krona' existe déjà

Code

krona(
  d_asv,
  ranks = c(11:19),
  "output/krona/OTU_post_clustered_allEuk",
  name = "OTU_post_clustered_allEuk"
)
krona(
  d,
  ranks = c(11:19),
  "output/krona/OTU_filtOnMaarjam_AM",
  name = "OTU_filtOnMaarjam_AM"
)
krona(
  d_muco,
  ranks = c(11:19),
  "output/krona/OTU_filtOnMaarjamAndPr2_AM",
  name = "OTU_filtOnMaarjamAndPr2_AM"
)
merge_krona(
  c(
    "output/krona/OTU_post_clustered_allEuk",
    "output/krona/OTU_filtOnMaarjam_AM",
    "output/krona/OTU_filtOnMaarjamAndPr2_AM"
  ),
  "output/krona/Euk_to_AM_filtering_nb_seq.html"
)

Number of OTU

Code

# Fig S8
krona(
  d_asv,
  ranks = c(11:19),
  "output/krona/OTU_post_clustered_allEuk_Nbotu",
  name = "OTU_post_clustered_allEuk",
  nb_seq = F
)
krona(
  d,
  ranks = c(11:19),
  "output/krona/OTU_filtOnMaarjam_AM_Nbotu",
  name = "OTU_filtOnMaarjam_AM",
  nb_seq = F
)
krona(
  d_muco,
  ranks = c(11:19),
  "output/krona/OTU_filtOnMaarjamAndPr2_AM_Nbotu",
  name = "OTU_filtOnMaarjamAndPr2_AM",
  nb_seq = F
)
merge_krona(
  c(
    "output/krona/OTU_post_clustered_allEuk_Nbotu",
    "output/krona/OTU_filtOnMaarjam_AM_Nbotu",
    "output/krona/OTU_filtOnMaarjamAndPr2_AM_Nbotu"
  ),
  "output/krona/Euk_to_AM_filtering_Nbotu.html"
)

Taxonomy

Code

table(d_rs@tax_table[,"Family"])


     Acaulosporaceae        Ambisporaceae     Archaeosporaceae 
                  36                    8                   56 
Claroideoglomeraceae     Diversisporaceae        Gigasporaceae 
                 260                  351                   20 
         Glomeraceae        Pacisporaceae      Paraglomeraceae 
                2855                   20                   81

Code

round(table(d_rs@tax_table[,"Family"])/ntaxa(d_rs)*100,2)


     Acaulosporaceae        Ambisporaceae     Archaeosporaceae 
                0.97                 0.22                 1.52 
Claroideoglomeraceae     Diversisporaceae        Gigasporaceae 
                7.04                 9.50                 0.54 
         Glomeraceae        Pacisporaceae      Paraglomeraceae 
               77.27                 0.54                 2.19

Code

round(table(d_rs@tax_table[,"Order"])/ntaxa(d_rs)*100,2)


Archaeosporales Diversisporales      Glomerales  Paraglomerales 
           1.73           11.56           84.30            2.19

Code

round(table(d_rs@tax_table[,"Genus"])/ntaxa(d_rs)*100,2)


     Acaulospora        Ambispora     Archaeospora  Claroideoglomus 
            0.97             0.22             1.52             6.74 
Claroideoglomus      Diversispora    Diversispora         Gigaspora 
            0.08             8.53             0.54             0.08 
          Glomus        Pacispora       Paraglomus    Scutellospora 
           76.89             0.54             2.19             0.46

Code

sort(round(table(d_rs@tax_table[,"Genus_PR2"])/ntaxa(d_rs)*100,2))


        Cetraspora          Gigaspora        Mortierella      Entrophospora 
              0.05               0.08               0.08               0.14 
     Scutellospora           Otospora          Pacispora       Archaeospora 
              0.24               0.27               0.51               0.57 
         Ambispora        Acaulospora       Sclerocystis         Paraglomus 
              0.60               0.78               0.78               0.81 
       Septoglomus Glomeromycotina_XX    Claroideoglomus       Diversispora 
              0.84               1.73               2.44               6.09 
     Funneliformis        Rhizophagus             Glomus 
              7.88              12.64              58.62

Code

sort(round(tapply(taxa_sums(d_rs), d_rs@tax_table[,"Family"], sum)/sum(d_rs@otu_table)*100,2))

       Ambisporaceae        Gigasporaceae      Acaulosporaceae 
                0.04                 0.21                 0.32 
       Pacisporaceae     Archaeosporaceae      Paraglomeraceae 
                0.51                 0.65                 2.61 
    Diversisporaceae Claroideoglomeraceae          Glomeraceae 
                3.88                10.83                80.95

Code

sort(round(tapply(taxa_sums(d_rs), d_rs@tax_table[,"Order"], sum)/sum(d_rs@otu_table)*100,2))

Archaeosporales  Paraglomerales Diversisporales      Glomerales 
           0.69            2.61            4.93           91.77

Code

sort(round(tapply(taxa_sums(d_rs), d_rs@tax_table[,"Genus"], sum)/sum(d_rs@otu_table)*100,2))

Claroideoglomus         Ambispora        Gigaspora    Scutellospora 
            0.01             0.04             0.05             0.16 
   Diversispora       Acaulospora        Pacispora     Archaeospora 
            0.23             0.32             0.51             0.65 
      Paraglomus     Diversispora  Claroideoglomus           Glomus 
            2.61             3.63            10.61            80.63

Code

sort(round(tapply(taxa_sums(d_rs), d_rs@tax_table[,"Genus_PR2"], sum)/sum(d_rs@otu_table)*100,2))

      Sclerocystis           Otospora         Cetraspora        Mortierella 
              0.00               0.01               0.02               0.02 
         Gigaspora      Entrophospora      Scutellospora       Archaeospora 
              0.05               0.07               0.08               0.23 
       Septoglomus        Acaulospora          Ambispora          Pacispora 
              0.29               0.32               0.45               0.51 
Glomeromycotina_XX         Paraglomus       Diversispora    Claroideoglomus 
              0.65               1.39               3.48               3.60 
     Funneliformis        Rhizophagus             Glomus 
             12.68              18.85              56.58

NUMBER OF SPORES ANALYSES

Code

psm_otu <- psmelt(as_binary_otu_table(d_spores)) |>
  filter(Abundance > 0) |>
  group_by(Sample) |>
  summarise(
    "Abundance" = sum(Abundance),
    "region" = region[1],
    "nb_spores" = nb_spores[1]
  )

Code

psm_res <- psmelt_samples_pq(d_spores) %>% mutate(nb_spores_log10 = log10(nb_spores))

Taxa are now in rows.

Joining with `by = join_by(Sample)`

Spores count <-> Alpha diversity

Code

# Fig S2a
ggstatsplot::ggscatterstats(psm_res, nb_spores_log10, Abundance, type = "non-parametric") +
  ggstatsplot::ggscatterstats(psm_res, nb_spores_log10, Hill_0, type = "non-parametric") +
  ggstatsplot::ggscatterstats(psm_res, nb_spores_log10, Hill_1, type = "non-parametric") +
  ggstatsplot::ggscatterstats(psm_res, nb_spores_log10, Hill_2, type = "non-parametric")

Registered S3 method overwritten by 'ggside':
  method from   
  +.gg   ggplot2

`stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.

Code

# Fig S2b
psm_res_rarefied <- psmelt_samples_pq(rarefy_even_depth(d_spores, rngseed = 221013)) %>% mutate(nb_spores_log10 = log10(nb_spores))

`set.seed(221013)` was used to initialize repeatable random subsampling.

Please record this for your records so others can reproduce.

Try `set.seed(221013); .Random.seed` for the full vector

...

490OTUs were removed because they are no longer 
present in any sample after random subsampling

...

Taxa are now in rows.

Joining with `by = join_by(Sample)`

Code

ggstatsplot::ggscatterstats(psm_res_rarefied, nb_spores_log10, Abundance, type = "non-parametric") +
  ggstatsplot::ggscatterstats(psm_res_rarefied, nb_spores_log10, Hill_0, type = "non-parametric") +
  ggstatsplot::ggscatterstats(psm_res_rarefied, nb_spores_log10, Hill_1, type = "non-parametric") +
  ggstatsplot::ggscatterstats(psm_res_rarefied, nb_spores_log10, Hill_2, type = "non-parametric")

`stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.

Spores count <-> Practice

Code

# Fig S5a
psm_res |> 
  mutate(is_organic = practice=="Organic") |>
  ggstatsplot::ggbetweenstats(is_organic, nb_spores_log10, type = "non-parametric")

Code

# Fig S5b
ggstatsplot::ggbetweenstats(psm_res, practice, nb_spores_log10, type = "non-parametric")

Spores count <-> Terroir

Code

# Fig. S4
ggstatsplot::ggbetweenstats(
  mutate(psm_res,
         terroir = reorder(psm_res$terroir, psm_res$nb_spores)
  ),
  terroir,
  nb_spores_log10,
  type = "non-parametric",
  centrality.plotting = F,
  package = "ggthemes",
  palette = "stata_economist"
) +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))

Spores count <-> Mycorrhization rate

Code

ggstatsplot::ggscatterstats(psm_res_rarefied, nb_spores_log10, Myc_freq, type = "non-parametric") +
  ggstatsplot::ggscatterstats(psm_res_rarefied, nb_spores_log10, Myc_intensity_colonization, type = "non-parametric") +
  ggstatsplot::ggscatterstats(psm_res_rarefied, nb_spores_log10, Arbuscul_abundance, type = "non-parametric")

`stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.

MYCORRHIZATION MEASURE

Mycorrhization rate <-> Alpha diversity

F = Myc_freq

Code

psm_res <- psmelt_samples_pq(d_rs)

Taxa are now in rows.

Joining with `by = join_by(Sample)`

Code

psm_res_rarefied <- psmelt_samples_pq(rarefy_even_depth(d_rs, rngseed = 221013))

`set.seed(221013)` was used to initialize repeatable random subsampling.
Please record this for your records so others can reproduce.
Try `set.seed(221013); .Random.seed` for the full vector
...
1269OTUs were removed because they are no longer 
present in any sample after random subsampling

...
Taxa are now in rows.
Joining with `by = join_by(Sample)`

Code

# Fig S3a
(ggstatsplot::ggscatterstats(psm_res, Myc_freq, Hill_0, type = "non-parametrique") +
    ggstatsplot::ggscatterstats(psm_res, Myc_freq, Hill_1, type = "non-parametrique") +
    ggstatsplot::ggscatterstats(psm_res, Myc_freq, Hill_2, type = "non-parametrique")) /
  (ggstatsplot::ggscatterstats(psm_res_rarefied, Myc_freq, Hill_0, type = "non-parametrique") +
     ggstatsplot::ggscatterstats(psm_res_rarefied, Myc_freq, Hill_1, type = "non-parametrique") +
     ggstatsplot::ggscatterstats(psm_res_rarefied, Myc_freq, Hill_2, type = "non-parametrique"))

`stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.

M = Myc_intensity_colonization

Code

# Fig S3b
(ggstatsplot::ggscatterstats(psm_res, Myc_intensity_colonization, Hill_0, type = "non-parametrique") +
    ggstatsplot::ggscatterstats(psm_res, Myc_intensity_colonization, Hill_1, type = "non-parametrique") +
    ggstatsplot::ggscatterstats(psm_res, Myc_intensity_colonization, Hill_2, type = "non-parametrique")) /
  (ggstatsplot::ggscatterstats(psm_res_rarefied, Myc_intensity_colonization, Hill_0, type = "non-parametrique") +
     ggstatsplot::ggscatterstats(psm_res_rarefied, Myc_intensity_colonization, Hill_1, type = "non-parametrique") +
     ggstatsplot::ggscatterstats(psm_res_rarefied, Myc_intensity_colonization, Hill_2, type = "non-parametrique"))

`stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.

A = Arbuscul_abundance

Code

# Fig S3c
(ggstatsplot::ggscatterstats(psm_res, Arbuscul_abundance , Hill_0, type = "non-parametrique") +
    ggstatsplot::ggscatterstats(psm_res, Arbuscul_abundance, Hill_1, type = "non-parametrique") +
    ggstatsplot::ggscatterstats(psm_res, Arbuscul_abundance, Hill_2, type = "non-parametrique")) /
  (ggstatsplot::ggscatterstats(psm_res_rarefied, Arbuscul_abundance, Hill_0, type = "non-parametrique") +
     ggstatsplot::ggscatterstats(psm_res_rarefied, Arbuscul_abundance, Hill_1, type = "non-parametrique") +
     ggstatsplot::ggscatterstats(psm_res_rarefied, Arbuscul_abundance, Hill_2, type = "non-parametrique"))

`stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.

Mycorrhization rate <-> terroir

Code

ggstatsplot::ggbetweenstats(psm_res_rarefied, terroir, Myc_freq, type = "non-parametrique", centrality.plotting = F) +
  ggstatsplot::ggbetweenstats(psm_res_rarefied, terroir, Myc_intensity_colonization, type = "non-parametrique", centrality.plotting = F) +
  ggstatsplot::ggbetweenstats(psm_res_rarefied, terroir, Arbuscul_abundance, type = "non-parametrique", centrality.plotting = F)

Number of labels is greater than default palette color count.
• Select another color `palette` (and/or `package`).
Number of labels is greater than default palette color count.
• Select another color `palette` (and/or `package`).
Number of labels is greater than default palette color count.
• Select another color `palette` (and/or `package`).

Code

kruskal.test(Myc_freq ~ terroir, psm_res_rarefied)


    Kruskal-Wallis rank sum test

data:  Myc_freq by terroir
Kruskal-Wallis chi-squared = 42.962, df = 13, p-value = 4.554e-05

Code

kruskal.test(Myc_intensity_colonization ~ terroir, psm_res_rarefied)


    Kruskal-Wallis rank sum test

data:  Myc_intensity_colonization by terroir
Kruskal-Wallis chi-squared = 21.13, df = 13, p-value = 0.07041

Code

kruskal.test(Arbuscul_abundance ~ terroir, psm_res_rarefied)


    Kruskal-Wallis rank sum test

data:  Arbuscul_abundance by terroir
Kruskal-Wallis chi-squared = 16.661, df = 13, p-value = 0.2153

Mycorrhization rate <-> practice

Code

ggstatsplot::ggbetweenstats(psm_res_rarefied, practice, Myc_freq, type = "non-parametrique", centrality.plotting = F) +
  ggstatsplot::ggbetweenstats(psm_res_rarefied, practice, Myc_intensity_colonization, type = "non-parametrique", centrality.plotting = F) +
  ggstatsplot::ggbetweenstats(psm_res_rarefied, practice, Arbuscul_abundance, type = "non-parametrique", centrality.plotting = F)

Code

kruskal.test(Myc_freq ~ practice, psm_res)


    Kruskal-Wallis rank sum test

data:  Myc_freq by practice
Kruskal-Wallis chi-squared = 6.152, df = 2, p-value = 0.04614

Code

kruskal.test(Myc_intensity_colonization ~ practice, psm_res)


    Kruskal-Wallis rank sum test

data:  Myc_intensity_colonization by practice
Kruskal-Wallis chi-squared = 19.667, df = 2, p-value = 5.361e-05

Code

kruskal.test(Arbuscul_abundance ~ practice, psm_res)


    Kruskal-Wallis rank sum test

data:  Arbuscul_abundance by practice
Kruskal-Wallis chi-squared = 17.449, df = 2, p-value = 0.0001626

Code

terroir_practice <- interaction(psm_res_rarefied$terroir, psm_res_rarefied$practice)
kruskal.test(Myc_freq ~ terroir_practice, psm_res_rarefied)


    Kruskal-Wallis rank sum test

data:  Myc_freq by terroir_practice
Kruskal-Wallis chi-squared = 51.416, df = 37, p-value = 0.0579

Code

kruskal.test(Myc_intensity_colonization ~ terroir_practice, psm_res_rarefied)


    Kruskal-Wallis rank sum test

data:  Myc_intensity_colonization by terroir_practice
Kruskal-Wallis chi-squared = 46.61, df = 37, p-value = 0.1337

Code

kruskal.test(Arbuscul_abundance ~ terroir_practice, psm_res_rarefied)


    Kruskal-Wallis rank sum test

data:  Arbuscul_abundance by terroir_practice
Kruskal-Wallis chi-squared = 42.772, df = 37, p-value = 0.2371

ECOLOGICAL ANALYSES : ALPHA DIVERSITY

Compartment

Code

psm_res <- psmelt_samples_pq(d_muco)

Taxa are now in rows.

Joining with `by = join_by(Sample)`

Code

psm_res_rarefied <- psmelt_samples_pq(rarefy_even_depth(d_muco, rngseed = 221013))

`set.seed(221013)` was used to initialize repeatable random subsampling.
Please record this for your records so others can reproduce.
Try `set.seed(221013); .Random.seed` for the full vector
...
1969OTUs were removed because they are no longer 
present in any sample after random subsampling

...
Taxa are now in rows.
Joining with `by = join_by(Sample)`

Code

ggstatsplot::ggbetweenstats(psm_res_rarefied,
                            compartment,
                            Hill_0,
                            type = "non-parametrique"
) +
  ggstatsplot::ggbetweenstats(psm_res_rarefied,
                              compartment,
                              Hill_1,
                              type = "non-parametrique"
  ) +
  ggstatsplot::ggbetweenstats(psm_res_rarefied,
                              compartment,
                              Hill_2,
                              type = "non-parametrique"
  )

Code

ggplot(psm_res_rarefied, aes(y=Hill_0, x=compartment, color=practice))+
  geom_point() + 
  geom_line(aes(group = paired_name)) +
  facet_wrap(~terroir)

Terroir

Code

psm_res <- psmelt_samples_pq(d_rs)

Taxa are now in rows.

Joining with `by = join_by(Sample)`

Code

psm_res_rarefied <- psmelt_samples_pq(rarefy_even_depth(d_rs, rngseed = 221013))

`set.seed(221013)` was used to initialize repeatable random subsampling.
Please record this for your records so others can reproduce.
Try `set.seed(221013); .Random.seed` for the full vector
...
1269OTUs were removed because they are no longer 
present in any sample after random subsampling

...
Taxa are now in rows.
Joining with `by = join_by(Sample)`

Code

# Figure S11b
(ggstatsplot::ggbetweenstats(
  mutate(
    psm_res_rarefied,
    terroir = reorder(psm_res_rarefied$terroir, psm_res_rarefied$Hill_2)
  ),
  terroir,
  Hill_0,
  type = "non-parametrique",
  centrality.plotting = F,
  package = "ggsci",
  palette = "springfield_simpsons",
  point.args = list(position = ggplot2::position_jitterdodge(dodge.width = 0.6), alpha =
                      0.8, size = 3, stroke = 0, na.rm = TRUE),
  violin.args = list(width=0),
  boxplot.args = list(alpha=0, width= 0.5)
)+ coord_flip()) /
  (ggstatsplot::ggbetweenstats(
    mutate(
      psm_res_rarefied,
      terroir = reorder(psm_res_rarefied$terroir, psm_res_rarefied$Hill_2)
    ),
    terroir,
    Hill_1,
    type = "non-parametrique",
    centrality.plotting = F,
    package = "ggsci",
    palette = "springfield_simpsons",
    point.args = list(position = ggplot2::position_jitterdodge(dodge.width = 0.6), alpha =
                        0.8, size = 3, stroke = 0, na.rm = TRUE),
    violin.args = list(width=0),
    boxplot.args = list(alpha=0, width= 0.5)
  )+ coord_flip()) /
  (ggstatsplot::ggbetweenstats(
    mutate(
      psm_res_rarefied,
      terroir = reorder(psm_res_rarefied$terroir, psm_res_rarefied$Hill_2)
    ),
    terroir,
    Hill_2,
    type = "non-parametrique",
    centrality.plotting = F,
    package = "ggsci",
    palette = "springfield_simpsons",
    point.args = list(position = ggplot2::position_jitterdodge(dodge.width = 0.6), alpha =
                        0.8, size = 3, stroke = 0, na.rm = TRUE),
    violin.args = list(width=0),
    boxplot.args = list(alpha=0, width= 0.5)
  ) + coord_flip()) + plot_layout(axes = "collect")

Code

# Figure S11a
(ggstatsplot::ggbetweenstats(
  mutate(
    psm_res,
    terroir = reorder(psm_res$terroir, psm_res$Hill_2)
  ),
  terroir,
  Hill_0,
  type = "non-parametrique",
  centrality.plotting = F,
  package = "ggsci",
  palette = "springfield_simpsons",
  point.args = list(position = ggplot2::position_jitterdodge(dodge.width = 0.6), alpha =
                      0.8, size = 3, stroke = 0, na.rm = TRUE),
  violin.args = list(width=0),
  boxplot.args = list(alpha=0, width= 0.5)
)+ coord_flip()) /
  (ggstatsplot::ggbetweenstats(
    mutate(
      psm_res,
      terroir = reorder(psm_res$terroir, psm_res$Hill_2)
    ),
    terroir,
    Hill_1,
    type = "non-parametrique",
    centrality.plotting = F,
    package = "ggsci",
    palette = "springfield_simpsons",
    point.args = list(position = ggplot2::position_jitterdodge(dodge.width = 0.6), alpha =
                        0.8, size = 3, stroke = 0, na.rm = TRUE),
    violin.args = list(width=0),
    boxplot.args = list(alpha=0, width= 0.5)
  )+ coord_flip()) /
  (ggstatsplot::ggbetweenstats(
    mutate(
      psm_res,
      terroir = reorder(psm_res$terroir, psm_res$Hill_2)
    ),
    terroir,
    Hill_2,
    type = "non-parametrique",
    centrality.plotting = F,
    package = "ggsci",
    palette = "springfield_simpsons",
    point.args = list(position = ggplot2::position_jitterdodge(dodge.width = 0.6), alpha =
                        0.8, size = 3, stroke = 0, na.rm = TRUE),
    violin.args = list(width=0),
    boxplot.args = list(alpha=0, width= 0.5)
  ) + coord_flip()) + plot_layout(axes = "collect")

Code

sort(tapply(psm_res_rarefied$Hill_0, psm_res_rarefied$terroir, median))

   Côte des Blancs            Vergèze               Tain Vallee de la Marne 
              25.0               53.0               68.0               69.5 
         Langoiran           Montcalm               Rian       Côte des Bar 
              75.0               75.5               79.0               82.5 
             Vosne           Mercurey           Faugeres           Cevennes 
              88.5               94.5              116.0              134.5 
      Aiguesmortes           Gigondas 
             158.5              167.5

Code

sort(tapply(psm_res$Hill_0, psm_res$terroir, median))

   Côte des Blancs            Vergèze               Tain Vallee de la Marne 
              26.0               55.0               76.0               78.5 
         Langoiran       Côte des Bar               Rian           Montcalm 
              84.0               94.5              102.0              107.0 
          Mercurey              Vosne           Faugeres       Aiguesmortes 
             108.5              111.5              210.5              242.0 
          Gigondas           Cevennes 
             249.5              263.0

Code

p_accu_terroir_rarefy <- MiscMetabar::accu_plot(rarefy_even_depth(d_rs, rngseed = 221013), "terroir", by.fact = T, step = 200) + xlim(c(0, 35000))

`set.seed(221013)` was used to initialize repeatable random subsampling.

Please record this for your records so others can reproduce.

Try `set.seed(221013); .Random.seed` for the full vector

...

1269OTUs were removed because they are no longer 
present in any sample after random subsampling

...

Code

threshold <- MiscMetabar::accu_samp_threshold(p_accu_terroir_rarefy)
sort(threshold)

          Montcalm       Aiguesmortes               Rian           Mercurey 
             10201              11001              13801              17001 
Vallee de la Marne       Côte des Bar               Tain              Vosne 
             18601              18801              19001              19401 
          Gigondas           Faugeres           Cevennes          Langoiran 
             21401              22001              23001              24001 
           Vergèze    Côte des Blancs 
             51401              53801

Code

p_accu_terroir_rarefy

Warning: Removed 702 rows containing missing values or values outside the scale range
(`geom_line()`).

Code

table(d_rs@sam_data$terroir)


      Aiguesmortes           Cevennes       Côte des Bar    Côte des Blancs 
                 2                  4                  4                 15 
          Faugeres           Gigondas          Langoiran           Mercurey 
                 4                  4                  5                  4 
          Montcalm               Rian               Tain Vallee de la Marne 
                 2                  3                  4                  4 
           Vergèze              Vosne 
                16                  4

Code

tapply(p_accu_terroir_rarefy$data$X1, p_accu_terroir_rarefy$data$fact, max)

      Aiguesmortes           Cevennes       Côte des Bar    Côte des Blancs 
               272                406                230                208 
          Faugeres           Gigondas          Langoiran           Mercurey 
               418                534                290                234 
          Montcalm               Rian               Tain Vallee de la Marne 
               143                277                221                220 
           Vergèze              Vosne 
               453                246

Code

# Fig_7a
p_accu_balanced_mod_terroir <- 
  MiscMetabar::accu_plot_balanced_modality(d_rs,
                                           "terroir", 
                                           nperm = 999, 
                                           step = 100,
                                           quantile_prob=0.9,
                                           sample.size = 10000)

`set.seed(1)` was used to initialize repeatable random subsampling.

Please record this for your records so others can reproduce.

Try `set.seed(1); .Random.seed` for the full vector

...

Warning in max(dff$xlab, na.rm = TRUE): aucun argument pour max ; -Inf est
renvoyé


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Code

p_accu_balanced_mod_terroir +
  xlab("Number of sequences") + 
  ylab("Number of OTUs") + 
  ggrepel::geom_label_repel(data=summarise(group_by(p_accu_balanced_mod_terroir$data,factor) ,y=max(X1,na.rm = T)),aes(label=factor,y=y,x=max(p_accu_balanced_mod_terroir$data$x,na.rm = T)), max.overlaps = 1900, force = 20) + xlim(c(1,25000)) + theme(legend.position = "none" )

Warning: Removed 14 rows containing missing values or values outside the scale range
(`geom_line()`).

Practice

Code

# Figure 4
ggstatsplot::ggbetweenstats(
  psm_res_rarefied,
  practice,
  Hill_0,
  centrality.plotting = F,
  type = "non-parametrique",
  package = "ggsci",
  palette = "springfield_simpsons",
  point.args = list(
    position = ggplot2::position_jitterdodge(dodge.width = 0.6),
    alpha =
      0.8,
    size = 3,
    stroke = 0,
    na.rm = TRUE
  ), ggplot.component = list(theme(plot.subtitle = element_text(size =5, face = "bold"))),
  violin.args = list(width = 0),
  boxplot.args = list(alpha = 0, width = 0.5)
) + ylab("Hill 0 (richness)") +
  ggstatsplot::ggbetweenstats(
    psm_res_rarefied,
    practice,
    Hill_1,
    centrality.plotting = F,
    type = "non-parametrique",
    package = "ggsci",
    palette = "springfield_simpsons",
    point.args = list(
      position = ggplot2::position_jitterdodge(dodge.width = 0.6),
      alpha =
        0.8,
      size = 3,
      stroke = 0,
      na.rm = TRUE
    ), ggplot.component = list(theme(plot.subtitle = element_text(size =5, face = "bold"))),
    violin.args = list(width = 0),
    boxplot.args = list(alpha = 0, width = 0.5)
  ) + ylab("Hill 1 (~Shannon)") +
  ggstatsplot::ggbetweenstats(
    psm_res_rarefied,
    practice,
    Hill_2,
    centrality.plotting = F,
    type = "non-parametrique",
    package = "ggsci",
    palette = "springfield_simpsons",
    point.args = list(
      position = ggplot2::position_jitterdodge(dodge.width = 0.6),
      alpha =
        0.8,
      size = 3,
      stroke = 0,
      na.rm = TRUE
    ), ggplot.component = list(theme(plot.subtitle = element_text(size =5, face = "bold"))),
    violin.args = list(width = 0),
    boxplot.args = list(alpha = 0, width = 0.5)
  ) + ylab("Hill 2 (~Simpson)") +  plot_layout(axes = "collect")

Code

# Figure S11
(ggstatsplot::ggbetweenstats(psm_res,
                             practice,
                             Hill_0,
                             centrality.plotting = F,
                             type = "non-parametrique",
                             violin.args = list(width = 0),
                             boxplot.args = list(alpha = 0, width = 0.5)
) + scale_color_manual(values=c("#0970aa9a", "#33bb9ec2", "#779d4aa3")) )/ (ggstatsplot::ggbetweenstats(psm_res,
                                                                                                        practice,
                                                                                                        Hill_1,
                                                                                                        centrality.plotting = F,
                                                                                                        type = "non-parametrique",
                                                                                                        violin.args = list(width = 0),
                                                                                                        boxplot.args = list(alpha = 0, width = 0.5)
) + scale_color_manual(values=c("#0970aa9a", "#33bb9ec2", "#779d4aa3"))) /
  ( ggstatsplot::ggbetweenstats(psm_res,
                                practice,
                                Hill_2,
                                centrality.plotting = F,
                                type = "non-parametrique",
                                violin.args = list(width = 0),
                                boxplot.args = list(alpha = 0, width = 0.5)
  ) + scale_color_manual(values=c("#0970aa9a", "#33bb9ec2", "#779d4aa3")))

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.
Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.
Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

Code

p_accu_practice_rarefy <- MiscMetabar::accu_plot(rarefy_even_depth(d_rs, rngseed = 221013), "practice", by.fact = T, step = 200)

`set.seed(221013)` was used to initialize repeatable random subsampling.

Please record this for your records so others can reproduce.

Try `set.seed(221013); .Random.seed` for the full vector

...

1269OTUs were removed because they are no longer 
present in any sample after random subsampling

...

Code

threshold <- MiscMetabar::accu_samp_threshold(p_accu_practice_rarefy)
sort(threshold)

  Conversion Conventional      Organic 
       88401        99001       194601

Code

p_accu_practice_rarefy

Warning: Removed 1 row containing missing values or values outside the scale range
(`geom_line()`).

Code

table(d_rs@sam_data$practice)


Conventional   Conversion      Organic 
          19           17           39

Code

tapply(p_accu_practice_rarefy$data$X1, p_accu_practice_rarefy$data$fact, max)

Conventional   Conversion      Organic 
         862          946         1461

Code

p_accu_practice_rarefy_after_merging <- MiscMetabar::accu_plot(rarefy_even_depth(merge_samples2(d_rs, "practice"), rngseed = 221013), "practice", step = 200)

`set.seed(221013)` was used to initialize repeatable random subsampling.

Please record this for your records so others can reproduce.

Try `set.seed(221013); .Random.seed` for the full vector

...

372OTUs were removed because they are no longer 
present in any sample after random subsampling

...

Warning in max(dff$xlab, na.rm = TRUE): aucun argument pour max ; -Inf est
renvoyé

Code

(p_accu_practice_rarefy + theme(legend.position = "none") + ylim(c(0, 125))) + (p_accu_practice_rarefy_after_merging + ylim(c(0, 125)))

Warning: Removed 2534 rows containing missing values or values outside the scale range
(`geom_line()`).

Warning: Removed 12577 rows containing missing values or values outside the scale range
(`geom_line()`).

Code

p_accu_practice_rarefy_after_merging

Warning: Removed 3 rows containing missing values or values outside the scale range
(`geom_line()`).

Code

# Fig_7b
p_accu_balanced_mod <- 
  MiscMetabar::accu_plot_balanced_modality(d_rs,
                                           "practice", 
                                           nperm = 999, 
                                           step = 300,
                                           quantile_prob=0.90)

`set.seed(1)` was used to initialize repeatable random subsampling.

Please record this for your records so others can reproduce.

Try `set.seed(1); .Random.seed` for the full vector

...

Warning in max(dff$xlab, na.rm = TRUE): aucun argument pour max ; -Inf est
renvoyé


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Code

(p_accu_balanced_mod +
    xlab("Number of sequences") +
    ylab("Number of OTUs")+ 
    ggrepel::geom_label_repel(data=summarise(group_by(p_accu_balanced_mod$data,factor) ,y=max(X1,na.rm = T)),aes(label=factor,y=y,x=max(p_accu_balanced_mod$data$x,na.rm = T)), max.overlaps = 1900, force = 20) + xlim(c(1,18000)) + theme(legend.position = "none" )) +
  (  p_accu_balanced_mod_terroir +
       xlab("Number of sequences") + 
       ylab("Number of OTUs") + 
       ggrepel::geom_label_repel(data=summarise(group_by(p_accu_balanced_mod_terroir$data,factor) ,y=max(X1,na.rm = T)),aes(label=factor,y=y,x=max(p_accu_balanced_mod_terroir$data$x,na.rm = T)), max.overlaps = 1900, force = 20) + xlim(c(1,25000)) + theme(legend.position = "none" ))

Warning: Removed 3 rows containing missing values or values outside the scale range
(`geom_line()`).

Warning: Removed 14 rows containing missing values or values outside the scale range
(`geom_line()`).

ZOOM ON LOW-DIVERSITY SAMPLES

Code

otu_hill <- vegan::renyi(d_rs@otu_table, scale = c(0, 1, 2), hill = TRUE)

barplot(sort(otu_hill$`0`))
abline(h = 10)

Code

barplot(sort(otu_hill$`1`))
abline(h = 5)

Code

barplot(sort(otu_hill$`2`))
abline(h = 5)

Code

low_div_samples <-
  rownames(otu_hill[
    otu_hill$`1` < 5 |
      otu_hill$`2` < 5, ])

low_div_samples

 [1] "2019--Languedoc--Cevennes--St Maurice de Cazevieille--Conventional--Conventional-- 4.247388--44.03523--Conventional--Herbicide--Scratching--Herbicide"    
 [2] "PE4"                                                                                                                                                      
 [3] "PE12"                                                                                                                                                     
 [4] "PE13"                                                                                                                                                     
 [5] "2023--Champagne--Côte des Blancs--Chetillons--Chetillons Ecorces--Conventional-- 4.034333--48.94572--Conventional--Herbicide--Grassing--Herbicide"        
 [6] "2023--Champagne--Côte des Blancs--Cramant--BIONNES JEB--Conversion-- 4.018985--48.98872--Conversion--Scratching--Grassing--Grassing"                      
 [7] "2023--Champagne--Côte des Blancs--Cramant--TDB AM29 JEB--Conversion-- 4.016860--48.99683--Conversion--Scratching--Grassing--Grassing"                     
 [8] "2023--Champagne--Côte des Blancs--Mireaux--JEB VOISIN 1 MIREAUX DESSOUS--Conventional-- 3.994335--48.98419--Conventional--Herbicide--Herbicide--Herbicide"
 [9] "2023--Champagne--Côte des Blancs--Avize--AVIZE Desh chimique en plein--Conventional-- 4.014914--48.97641--Conventional--Herbicide--Herbicide--Herbicide"  
[10] "2020--Bordelais--Langoiran--Langoiran--Conventional--Conventional---0.384545--44.70287--Conventional--Scratching--Grassing--Grassing"                     
[11] "2020--Bordelais--Langoiran--Langoiran--Conventional chimique--Conventional---0.356503--44.71320--Conventional--Herbicide--Herbicide--Herbicide"           
[12] "2020--Bordelais--Rian--Rian--Organic grassed--Organic---0.337082--44.66779--Organic grassed--Scratching--Grassing--Grassing"                              
[13] "2020--Bordelais--Rian--Rian--Conventional Terra Vitis--Conventional---0.333922--44.66419--Organic grassed--Scratching--Grassing--Grassing"

Code

sum(otu_hill$`0` < 10)

[1] 2

Code

sum(otu_hill$`1` < 5)

[1] 1

Code

sum(otu_hill$`2` < 5)

[1] 13

Code

length(low_div_samples)

[1] 13

Code

low_div_samples_table <- tibble(cbind(as_tibble(d_rs@sam_data[low_div_samples, c("terroir", "practice", "rank", "inter_rank", "compartment")]), apply(otu_hill[low_div_samples, ], 2, round, digits = 2), sample_sums(d_rs)[low_div_samples]))

Warning in class(x) <- tibble_class: La class(x) est une chaîne multiple
("tbl_df", "tbl", ...) ; le résultat ne sera plus un objet S4

Code

low_div_samples_table$compartment <- ifelse(is.na(low_div_samples_table$compartment), "Spores + Roots", "Spores")

colnames(low_div_samples_table) <- c("Terroir", "Global practice", "Rank practice", "Inter rank practice", "Compartment", "Hill 0 (richness)", "Hill 1 (~Shannon)", "Hill 2 (~Simpson)", "nb_seq")

low_div_samples_table <- arrange(low_div_samples_table, as.numeric(`Hill 0 (richness)`))

low_div_samples_table <-
  rbind(
    low_div_samples_table,
    c(
      "",
      "",
      "",
      "MEAN",
      "(low -diversity samples)",
      round(mean(low_div_samples_table$`Hill 0 (richness)`), 2),
      round(mean(low_div_samples_table$`Hill 1 (~Shannon)`), 2),
      round(mean(low_div_samples_table$`Hill 2 (~Simpson)`), 2),
      round(mean(low_div_samples_table$nb_seq), 2)
    ),
    c(
      "",
      "",
      "",
      "MEAN",
      "(all samples)",
      round(mean(otu_hill$`0`), 2),
      round(mean(otu_hill$`1`), 2),
      round(mean(otu_hill$`2`), 2),
      round(mean(sample_sums(d_rs)), 2)
    ),
    c(
      "",
      "",
      "",
      "MAX",
      "(all samples)",
      round(max(otu_hill$`0`), 2),
      round(max(otu_hill$`1`), 2),
      round(max(otu_hill$`2`), 2),
      round(max(sample_sums(d_rs)), 2)
    )
  )

# Table XXX_low_div_samples
kbl(low_div_samples_table) |>
  kable_classic(full_width = F, html_font = "Cambria")

Terroir	Global practice	Rank practice	Inter rank practice	Compartment	Hill 0 (richness)	Hill 1 (~Shannon)	Hill 2 (~Simpson)	nb_seq
Langoiran	Conventional	Herbicide	Herbicide	Spores + Roots	6	3.05	2.11	12643
Vergèze	Organic	Scratching	Ploughing	Spores	8	5.8	4.76	6768
Côte des Blancs	Conventional	Herbicide	Grassing	Spores + Roots	11	5.58	4.09	28481
Côte des Blancs	Conversion	Scratching	Grassing	Spores + Roots	11	6.59	4.65	42775
Vergèze	Organic	Scratching	Grassing	Spores + Roots	18	6.2	3.55	49579
Côte des Blancs	Conversion	Scratching	Grassing	Spores + Roots	18	5.82	4.53	45845
Côte des Blancs	Conventional	Herbicide	Herbicide	Spores + Roots	18	5.68	3.52	41991
Vergèze	Organic	Scratching	Scratching	Spores + Roots	27	6.36	2.98	34242
Côte des Blancs	Conventional	Herbicide	Herbicide	Spores + Roots	27	6.28	3.59	45017
Rian	Organic	Scratching	Grassing	Spores + Roots	70	13.21	4.5	42986
Cevennes	Conventional	Herbicide	Scratching	Spores + Roots	71	8.56	4.95	93308
Langoiran	Conventional	Scratching	Grassing	Spores + Roots	81	10.24	3.84	36512
Rian	Conventional	Scratching	Grassing	Spores + Roots	102	10.47	3.65	70525
			MEAN	(low -diversity samples)	36	7.22	3.9	42359.38
			MEAN	(all samples)	103.09	20.14	10.47	48294.53
			MAX	(all samples)	443	64.4	24.16	101183

ECOLOGICAL ANALYSES : BETA DIVERSITY

Compartment

Code

# Fig S10
p <- phyloseq::plot_ordination(d_muco,
                               vegan::decorana(vegdist(as(otu_table(d_muco), "matrix"),
                                                       method = "robust.aitchison"
                               )),
                               color = "region",
                               shape = "compartment"
) +
  geom_point(size = 3) +
  stat_ellipse(inherit.aes = F, aes(x = DCA1, y = DCA2, linetype = compartment))

Warning in phyloseq::plot_ordination(d_muco,
vegan::decorana(vegdist(as(otu_table(d_muco), : `Ordination species/OTU/taxa
coordinate indices did not match `physeq` index names. Setting corresponding
coordinates to NULL.

Code

p + geom_line(data = p$data, aes(group = paired_name))

Practice and terroir

Code

p <-
  phyloseq::plot_ordination(d_rs,
                            ordinate(d_rs,
                                     method = "NMDS",
                                     distance = "bray"
                            ),
                            color = "terroir",
                            shape = "practice"
  ) + facet_wrap("region") + scale_shape_manual(values=c(25,23,24))

Square root transformation
Wisconsin double standardization
Run 0 stress 0.2199813 
Run 1 stress 0.2448036 
Run 2 stress 0.2180221 
... New best solution
... Procrustes: rmse 0.06822839  max resid 0.5284097 
Run 3 stress 0.216845 
... New best solution
... Procrustes: rmse 0.02068218  max resid 0.116768 
Run 4 stress 0.2168451 
... Procrustes: rmse 0.0002339127  max resid 0.001590561 
... Similar to previous best
Run 5 stress 0.2199347 
Run 6 stress 0.2381263 
Run 7 stress 0.2176648 
Run 8 stress 0.2206405 
Run 9 stress 0.2199851 
Run 10 stress 0.2177386 
Run 11 stress 0.2382035 
Run 12 stress 0.2505707 
Run 13 stress 0.2186254 
Run 14 stress 0.2260154 
Run 15 stress 0.2168451 
... Procrustes: rmse 0.0001809623  max resid 0.001056322 
... Similar to previous best
Run 16 stress 0.2200828 
Run 17 stress 0.225982 
Run 18 stress 0.2488814 
Run 19 stress 0.2168449 
... New best solution
... Procrustes: rmse 0.0002316716  max resid 0.001384702 
... Similar to previous best
Run 20 stress 0.2168451 
... Procrustes: rmse 0.0002848589  max resid 0.001564728 
... Similar to previous best
*** Best solution repeated 2 times

Code

p_nmds <- p + geom_point(
  data = select(p$data, -c(region)),
  fill = "grey40",
  color = "grey20",
  size = 1,
  alpha = 0.5
) +
  geom_point(size = 3, aes(fill=terroir)) +
  ggtitle("NMDS on bray distance") + 
  scale_fill_manual(values=c("#5660d0","#de8a00","#b59e00","#00773d","#fcb709",
                             "#e15968","#784b43","#c200ab","#00b3f0","#953726",
                             "#e09199","#379d30","#ed5019","#ff63b0")) +
  scale_color_manual(values=rep("grey20",14))


# XXXX_nmds_region_practice_terroir
p_nmds

Code

p <- phyloseq::plot_ordination(d_rs,
                               ordinate(d_rs,
                                        method = "NMDS",
                                        distance = "bray"
                               ),
                               color = "practice"
) +
  geom_point(size = 4) + facet_wrap("~terroir")

Square root transformation
Wisconsin double standardization
Run 0 stress 0.2199813 
Run 1 stress 0.2218407 
Run 2 stress 0.2257996 
Run 3 stress 0.2168451 
... New best solution
... Procrustes: rmse 0.06699764  max resid 0.5255248 
Run 4 stress 0.2201833 
Run 5 stress 0.2182975 
Run 6 stress 0.2505065 
Run 7 stress 0.2183028 
Run 8 stress 0.2222091 
Run 9 stress 0.2205322 
Run 10 stress 0.2256243 
Run 11 stress 0.2198921 
Run 12 stress 0.220747 
Run 13 stress 0.228605 
Run 14 stress 0.2279042 
Run 15 stress 0.2201713 
Run 16 stress 0.2182979 
Run 17 stress 0.227071 
Run 18 stress 0.219269 
Run 19 stress 0.2168449 
... New best solution
... Procrustes: rmse 0.0002240927  max resid 0.001403987 
... Similar to previous best
Run 20 stress 0.2350719 
*** Best solution repeated 1 times

Code

p + geom_point(
  data = select(p$data, -c(terroir)),
  size = 2,
  alpha = 0.3
) +
  geom_point(size = 4) +
  ggtitle("NMDS on bray distance")

Code

# Fig 6a
upset_pq(d_rs, "practice", taxa_fill = "Family",
         set_sizes=(
           upset_set_size()
           + geom_text(aes(label=after_stat(count)), hjust=-0.3, color="white", stat='count')
         ))

Cleaning suppress 0 taxa (  ) and 0 sample(s) (  ).

Number of non-matching ASV 0

Number of matching ASV 3695

Number of filtered-out ASV 0

Number of kept ASV 3695

Number of kept samples 75

Code

ggvenn_pq(d_rs, "practice")

Two modalities differ greatly (more than x2) in their number of sequences (1865101 vs 838771). You may be interested by the parameter rarefy_after_merging

Code

# Fig 6b
upset_pq(d_rs, "terroir", taxa_fill = "Family", min_size = 2,  height_ratio = 0.6
         ,set_sizes=(
           upset_set_size()
           + geom_label(aes(label=after_stat(count)), hjust=-0.3, size=2.5,  stat='count')
         ))

Cleaning suppress 0 taxa (  ) and 0 sample(s) (  ).

Number of non-matching ASV 0

Number of matching ASV 3695

Number of filtered-out ASV 0

Number of kept ASV 3695

Number of kept samples 75

Warning: Removed 232 rows containing non-finite outside the scale range
(`stat_count()`).

Global beta-div analysis

Spatial data

Code

library(geodist)
lat_lon <- as_tibble(d_rs@sam_data[, c("lat", "long")])
lat_lon$lat <- as.numeric(lat_lon$lat)
lat_lon$long <- as.numeric(lat_lon$long)
dist_spatial_meter <- as.dist(geodist(lat_lon, measure = "geodesic"),
                              upper = FALSE
)

Code

lon_lat_rs <- d_rs@sam_data[, c("long", "lat")]
lon_lat_rs$long <- as.numeric(lon_lat_rs$long)
lon_lat_rs$lat <- as.numeric(lon_lat_rs$lat)
MEM <- dbmem(lon_lat_rs, MEM.autocor = "non-null")

Code

test_MEM <- moran.randtest(MEM, nrepet = 1999)
test_MEM$pvalue_adjust <- p.adjust(test_MEM$pvalue, method = "BH")
test_MEM$pvalue_adjust[test_MEM$pvalue_adjust < 0.05]

[1] 0.0185 0.0185

Code

barplot(test_MEM$obs)

Code

d_rs@sam_data$MEM_1 <- MEM[, 1]
d_rs@sam_data$MEM_2 <- MEM[, 2]
res_ado_spatial_robAit <- adonis_pq(
  d_rs,
  "MEM_1 + MEM_2 + practice + inter_rank + rank  + terroir",
  correction_for_sample_size = TRUE,
  dist_method = "robust.aitchison",
  by = "terms"
)

res_ado_spatial_robAit_rarefy <- adonis_pq(
  rarefy_even_depth(d_rs, rngseed = 626),
  "MEM_1 + MEM_2 + practice + inter_rank + rank  + terroir",
  correction_for_sample_size = FALSE,
  dist_method = "robust.aitchison",
  by = "terms"
)

`set.seed(626)` was used to initialize repeatable random subsampling.

Please record this for your records so others can reproduce.

Try `set.seed(626); .Random.seed` for the full vector

...

1258OTUs were removed because they are no longer 
present in any sample after random subsampling

...

Code

res_ado_spatial_bray <- adonis_pq(
  d_rs,
  "MEM_1 + MEM_2 + practice + inter_rank + rank  + terroir",
  correction_for_sample_size = TRUE,
  dist_method = "bray",
  by = "terms"
)


res_ado_spatial_bray_rarefy <- adonis_pq(
  rarefy_even_depth(d_rs, rngseed = 626),
  "MEM_1 + MEM_2 + practice + inter_rank + rank  + terroir",
  correction_for_sample_size = FALSE,
  dist_method = "bray",
  by = "terms"
)

`set.seed(626)` was used to initialize repeatable random subsampling.

Please record this for your records so others can reproduce.

Try `set.seed(626); .Random.seed` for the full vector

...

1258OTUs were removed because they are no longer 
present in any sample after random subsampling

...

Code

df <- data.frame(res_ado_spatial_bray)
df$names <-
  factor(
    rownames(df),
    levels = c(
      "sample_size",
      "MEM_1",
      "MEM_2",
      "practice",
      "inter_rank",
      "rank",
      "terroir",
      "Residual",
      "Total"
    )
  )
df <- df %>% filter(!names == "Total")
p1 <-
  ggplot(df, aes(y = R2 / Df, x = names, fill = R2 / Df)) +
  geom_bar(stat = "identity") +
  scale_fill_viridis_c() +
  geom_text(
    label = case_when(
      df$Pr..F. < 0.001 ~ "***",
      df$Pr..F. < 0.01 ~ "**",
      df$Pr..F. < 0.05 ~ "*",
      df$Pr..F. > 0.05 ~ "ns"
    ),
    nudge_y = 0.01
  ) +
  ggtitle("Permanova on Bray")

df <- data.frame(res_ado_spatial_robAit)
df$names <-
  factor(
    rownames(df),
    levels = c(
      "MEM_1",
      "MEM_2",
      "practice",
      "inter_rank",
      "rank",
      "terroir",
      "Residual",
      "Total"
    )
  )
df <- df %>% filter(!names == "Total")
p2 <-
  ggplot(df, aes(y = R2 / Df, x = names, fill = R2 / Df)) +
  geom_bar(stat = "identity") +
  scale_fill_viridis_c() +
  geom_text(
    label = case_when(
      df$Pr..F. < 0.001 ~ "***",
      df$Pr..F. < 0.01 ~ "**",
      df$Pr..F. < 0.05 ~ "*",
      df$Pr..F. > 0.05 ~ "ns"
    ),
    nudge_y = 0.01
  ) +
  ggtitle("Permanova on robust Aitchison")

df <- data.frame(res_ado_spatial_bray_rarefy)
df$names <-
  factor(
    rownames(df),
    levels = c(
      "MEM_1",
      "MEM_2",
      "practice",
      "inter_rank",
      "rank",
      "terroir",
      "Residual",
      "Total"
    )
  )
df <- df %>% filter(!names == "Total")

p3 <-
  ggplot(df, aes(y = R2 / Df, x = names, fill = R2 / Df)) +
  geom_bar(stat = "identity") +
  scale_fill_viridis_c() +
  geom_text(
    label = case_when(
      df$Pr..F. < 0.001 ~ "***",
      df$Pr..F. < 0.01 ~ "**",
      df$Pr..F. < 0.05 ~ "*",
      df$Pr..F. > 0.05 ~ "ns"
    ),
    nudge_y = 0.01
  ) +
  ggtitle("Permanova on Bray after rarefaction")

df <- data.frame(res_ado_spatial_robAit_rarefy)
df$names <-
  factor(
    rownames(df),
    levels = c(
      "MEM_1",
      "MEM_2",
      "practice",
      "inter_rank",
      "rank",
      "terroir",
      "Residual",
      "Total"
    )
  )
df <- df %>% filter(!names == "Total")
p4 <-
  ggplot(df, aes(y = R2 / Df, x = names, fill = R2 / Df)) +
  geom_bar(stat = "identity") +
  scale_fill_viridis_c() +
  geom_text(
    label = case_when(
      df$Pr..F. < 0.001 ~ "***",
      df$Pr..F. < 0.01 ~ "**",
      df$Pr..F. < 0.05 ~ "*",
      df$Pr..F. > 0.05 ~ "ns"
    ),
    nudge_y = 0.01
  ) +
  ggtitle("Permanova on robust Aitchison after rarefaction")

(p1 + ylim(0, 0.12) + p2 + ylim(0, 0.12)) /
  (p3 + ylim(0, 0.12) + p4 + ylim(0, 0.12))

Warning: Removed 1 row containing missing values or values outside the scale range
(`geom_text()`).
Removed 1 row containing missing values or values outside the scale range
(`geom_text()`).
Removed 1 row containing missing values or values outside the scale range
(`geom_text()`).
Removed 1 row containing missing values or values outside the scale range
(`geom_text()`).

Code

anova(vegan::betadisper(phyloseq::distance(d_rs@otu_table, "bray"), d_rs@sam_data$terroir))

Analysis of Variance Table

Response: Distances
          Df  Sum Sq  Mean Sq F value Pr(>F)
Groups    13 0.23559 0.018122  1.4572 0.1605
Residuals 61 0.75865 0.012437

Code

anova(vegan::betadisper(phyloseq::distance(d_rs@otu_table, "bray"), d_rs@sam_data$practice))

Analysis of Variance Table

Response: Distances
          Df  Sum Sq   Mean Sq F value Pr(>F)
Groups     2 0.01743 0.0087135  0.8894 0.4154
Residuals 72 0.70541 0.0097974

Code

anova(vegan::betadisper(phyloseq::distance(d_rs@otu_table, "bray"), rarefy_even_depth(d_rs, rngseed = 626)@sam_data$terroir))

`set.seed(626)` was used to initialize repeatable random subsampling.

Please record this for your records so others can reproduce.

Try `set.seed(626); .Random.seed` for the full vector

...

1258OTUs were removed because they are no longer 
present in any sample after random subsampling

...

Analysis of Variance Table

Response: Distances
          Df  Sum Sq  Mean Sq F value Pr(>F)
Groups    13 0.23559 0.018122  1.4572 0.1605
Residuals 61 0.75865 0.012437

Code

anova(vegan::betadisper(phyloseq::distance(d_rs@otu_table, "bray"), rarefy_even_depth(d_rs, rngseed = 626)@sam_data$practice))

`set.seed(626)` was used to initialize repeatable random subsampling.

Please record this for your records so others can reproduce.

Try `set.seed(626); .Random.seed` for the full vector

...

1258OTUs were removed because they are no longer 
present in any sample after random subsampling

...

Analysis of Variance Table

Response: Distances
          Df  Sum Sq   Mean Sq F value Pr(>F)
Groups     2 0.01743 0.0087135  0.8894 0.4154
Residuals 72 0.70541 0.0097974

Code

anova(vegan::betadisper(vegdist(d_rs@otu_table, "robust.aitchison"), d_rs@sam_data$terroir))

Analysis of Variance Table

Response: Distances
          Df  Sum Sq Mean Sq F value    Pr(>F)    
Groups    13 2529.97 194.613  13.879 1.127e-13 ***
Residuals 61  855.37  14.022                      
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Code

anova(vegan::betadisper(vegdist(d_rs@otu_table, "robust.aitchison"), d_rs@sam_data$practice))

Analysis of Variance Table

Response: Distances
          Df Sum Sq Mean Sq F value Pr(>F)
Groups     2   69.5  34.728  0.4285 0.6532
Residuals 72 5835.7  81.051

Code

anova(vegan::betadisper(vegdist(d_rs@otu_table, "robust.aitchison"), rarefy_even_depth(d_rs, rngseed = 626)@sam_data$terroir))

`set.seed(626)` was used to initialize repeatable random subsampling.

Please record this for your records so others can reproduce.

Try `set.seed(626); .Random.seed` for the full vector

...

1258OTUs were removed because they are no longer 
present in any sample after random subsampling

...

Analysis of Variance Table

Response: Distances
          Df  Sum Sq Mean Sq F value    Pr(>F)    
Groups    13 2529.97 194.613  13.879 1.127e-13 ***
Residuals 61  855.37  14.022                      
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Code

anova(vegan::betadisper(vegdist(d_rs@otu_table, "robust.aitchison"), rarefy_even_depth(d_rs, rngseed = 626)@sam_data$practice))

`set.seed(626)` was used to initialize repeatable random subsampling.

Please record this for your records so others can reproduce.

Try `set.seed(626); .Random.seed` for the full vector

...

1258OTUs were removed because they are no longer 
present in any sample after random subsampling

...

Analysis of Variance Table

Response: Distances
          Df Sum Sq Mean Sq F value Pr(>F)
Groups     2   69.5  34.728  0.4285 0.6532
Residuals 72 5835.7  81.051

Soil properties

Code

soil_prop <- as_tibble(d_rs@sam_data) |>
  select(paired_name, practice, organic, terroir, Coarse_sand:CEC)
soil_prop_num <- soil_prop |>
  select(-all_of(c("practice", "organic", "terroir", "CaO", "Total_sand", "Total_filt"))) |>
  tibble::column_to_rownames("paired_name") |>
  mutate(across(everything(), as.numeric)) |>
  tidyr::drop_na()

Code

#devtools::install_github("arleyc/PCAtest")
pca_test_res <- PCAtest::PCAtest(soil_prop_num, varcorr = T, plot = F)


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Comparing empirical statistics with their null distributions... Please wait

========================================================
Test of PCA significance: 17 variables, 49 observations
1000 bootstrap replicates, 1000 random permutations
========================================================

Empirical Psi = 57.6945, Max null Psi = 7.9011, Min null Psi = 3.9156, p-value = 0
Empirical Phi = 0.4606, Max null Phi = 0.1704, Min null Phi = 0.1200, p-value = 0

Empirical eigenvalue #1 = 7.79931, Max null eigenvalue = 2.7847, p-value = 0
Empirical eigenvalue #2 = 2.64507, Max null eigenvalue = 2.30548, p-value = 0
Empirical eigenvalue #3 = 1.91872, Max null eigenvalue = 2.00799, p-value = 0.009
Empirical eigenvalue #4 = 1.04069, Max null eigenvalue = 1.75936, p-value = 1
Empirical eigenvalue #5 = 0.90745, Max null eigenvalue = 1.56975, p-value = 1
Empirical eigenvalue #6 = 0.76203, Max null eigenvalue = 1.43569, p-value = 1
Empirical eigenvalue #7 = 0.61678, Max null eigenvalue = 1.30771, p-value = 1
Empirical eigenvalue #8 = 0.37522, Max null eigenvalue = 1.1816, p-value = 1
Empirical eigenvalue #9 = 0.25142, Max null eigenvalue = 1.09965, p-value = 1
Empirical eigenvalue #10 = 0.23602, Max null eigenvalue = 0.96987, p-value = 1
Empirical eigenvalue #11 = 0.18674, Max null eigenvalue = 0.8615, p-value = 1
Empirical eigenvalue #12 = 0.12613, Max null eigenvalue = 0.75665, p-value = 1
Empirical eigenvalue #13 = 0.08683, Max null eigenvalue = 0.70178, p-value = 1
Empirical eigenvalue #14 = 0.04197, Max null eigenvalue = 0.6121, p-value = 1
Empirical eigenvalue #15 = 0.00562, Max null eigenvalue = 0.52401, p-value = 1
Empirical eigenvalue #16 = 0, Max null eigenvalue = 0.41784, p-value = 1
Empirical eigenvalue #17 = 0, Max null eigenvalue = 0.37203, p-value = 1

PC 1 is significant and accounts for 45.9% (95%-CI:41.4-51.4) of the total variation
PC 2 is significant and accounts for 15.6% (95%-CI:13.7-19) of the total variation
PC 3 is significant and accounts for 11.3% (95%-CI:9-13.3) of the total variation

The first 3 PC axes are significant and account for 72.7% of the total variation

Variables 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, and 17 have significant loadings on PC 1
Variables 1, 4, 12, 13, and 16 have significant loadings on PC 2
Variables 3, 6, and 7 have significant loadings on PC 3

Variables 1, and 2 have significant correlations with PC 1
Variables 12, and 13 have significant correlations with PC 2
Variables 6, and 7 have significant correlations with PC 3

Code

pval <- c()
for (i in seq_len(length(pca_test_res$`Percentage of variation of empirical PC's`))) {
  obs <- pca_test_res$`Percentage of variation of empirical PC's`[i]
  null_model <- pca_test_res$`Percentage of variation of randomized data`[, i]
  
  pval[i] <- (sum(obs < null_model) + 1) / (1 + nrow(pca_test_res$`Percentage of variation of randomized data`))
}

pca_test_pval_adj <- p.adjust(pval, method = "BH")
pca_test_pval_adj

 [1] 0.008491508 0.008491508 0.056610057 1.000000000 1.000000000 1.000000000
 [7] 1.000000000 1.000000000 1.000000000 1.000000000 1.000000000 1.000000000
[13] 1.000000000 1.000000000 1.000000000 1.000000000 1.000000000

Code

res_pca <- dudi.pca(soil_prop_num, scannf = F, nf = 4)

Code

p_pca_variable <- fviz_pca_var(res_pca,
                               col.var = "cos2",
                               gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
                               repel = TRUE
)

p_pca_variable_axe3_4 <- fviz_pca_var(res_pca,
                                      col.var = "cos2", axes = c(3, 4),
                                      gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
                                      repel = TRUE
)

res_pca_var <- get_pca_var(res_pca)

# Fig. 2
(p_pca_variable + p_pca_variable_axe3_4) / free((factoextra::fviz_eig(res_pca)) +
                                                  (ggcorrplot::ggcorrplot(res_pca_var$cor)))

Code

practice <- unlist(lapply(strsplit(rownames(res_pca$tab), "--"), function(x) {
  x[6]
}))

terroir <- unlist(lapply(strsplit(rownames(res_pca$tab), "--"), function(x) {
  x[3]
}))

p_pca_ind_terroir <- fviz_pca_ind(res_pca,
                                  habillage = terroir,
                                  label = FALSE,
                                  repel = TRUE,
                                  mean.point = FALSE,
                                  addEllipses = TRUE,
                                  pointsize = 3,
                                  ellipse.type = "convex"
) +
  scale_shape_manual(values = rep(16, length(unique(terroir))))

Scale for shape is already present.
Adding another scale for shape, which will replace the existing scale.

Code

p_pca_ind_terroir$data$terroir <- terroir
p_pca_ind_terroir$data$practice <- practice

p_pca_ind_terroir_practice <- p_pca_ind_terroir + geom_point(aes(x = x, y = y),
                                                             shape = case_when(
                                                               practice == "Organic" ~ 2,
                                                               practice == "Conventional" ~ 6,
                                                               practice == "Conversion" ~ 5
                                                             ), size = 3.5,
                                                             color = rgb(0, 0, 0, 0.7)
)

fviz_pca_var(res_pca)

Code

# value
kw1_terroir <- kruskal.test(res_pca$tab[, 1], terroir)
kw2_terroir <- kruskal.test(res_pca$tab[, 2], terroir)
kw3_terroir <- kruskal.test(res_pca$tab[, 3], terroir)

kw1_practice <- kruskal.test(res_pca$tab[, 1], practice)
kw2_practice <- kruskal.test(res_pca$tab[, 2], practice)
kw3_practice <- kruskal.test(res_pca$tab[, 3], practice)


# Fig. 3 -> now figure S
p_pca_ind_terroir_practice + 
  annotate("text", x = 2, y = 4.5, label = paste("Kruskal-Wallis (axe1 - terroir): pval=", round(as.numeric(kw1_terroir$p.value),4)), size = 3) + 
  annotate("text", x = 2, y = 4.25, label = paste("Kruskal-Wallis (axe2 - terroir): pval=", round(as.numeric(kw2_terroir$p.value),4)), size = 3) + 
  annotate("text", x = 2, y = 4, label = paste("Kruskal-Wallis (axe3 - terroir): pval=", round(as.numeric(kw3_terroir$p.value),4)), size = 3) + 
  annotate("text", x = -2, y = 4.5, label = paste("Kruskal-Wallis (axe1 - practice): pval=", round(as.numeric(kw1_practice$p.value),4)), size = 3) + 
  annotate("text", x = -2, y = 4.25, label = paste("Kruskal-Wallis (axe2 - practice): pval=", round(as.numeric(kw2_practice$p.value),4)), size = 3) + 
  annotate("text", x = -2, y = 4, label = paste("Kruskal-Wallis (axe3 - practice): pval=", round(as.numeric(kw3_practice$p.value),4)), size = 3)

Warning: Removed 5 rows containing missing values or values outside the scale range
(`geom_point()`).
Removed 5 rows containing missing values or values outside the scale range
(`geom_point()`).

Code

# Fig. S1
ggplot(d_rs@sam_data, aes(x = practice, y = as.numeric(Cu), fill = practice)) +
  geom_violin() +
  geom_jitter()

Warning: Removed 26 rows containing non-finite outside the scale range
(`stat_ydensity()`).

Warning: Removed 26 rows containing missing values or values outside the scale range
(`geom_point()`).

Code

# value
kruskal.test(as.numeric(d_rs@sam_data$Cu), d_rs@sam_data$practice)


    Kruskal-Wallis rank sum test

data:  as.numeric(d_rs@sam_data$Cu) and d_rs@sam_data$practice
Kruskal-Wallis chi-squared = 0.61, df = 2, p-value = 0.7371

Code

d_with_pca <-
  clean_pq(subset_samples_pq(d_rs, d_rs@sam_data$paired_name %in% rownames(soil_prop_num)))

Cleaning suppress 192 taxa and 0 samples.

Code

res_pca_ind <- get_pca_ind(res_pca)

if (!identical(match(rownames(res_pca_ind$coord), d_with_pca@sam_data$paired_name), sort(match(rownames(res_pca_ind$coord), d_with_pca@sam_data$paired_name)))) {
  stop("ERROR")
}

d_with_pca <- add_info_to_sam_data(d_with_pca, res_pca_ind$coord)

cor.test(d_with_pca@sam_data$Dim.1, d_with_pca@sam_data$nb_otu, method = "spearman")

Warning in cor.test.default(d_with_pca@sam_data$Dim.1,
d_with_pca@sam_data$nb_otu, : Impossible de calculer la p-value exacte avec des
ex-aequos


    Spearman's rank correlation rho

data:  d_with_pca@sam_data$Dim.1 and d_with_pca@sam_data$nb_otu
S = 20547, p-value = 0.7416
alternative hypothesis: true rho is not equal to 0
sample estimates:
        rho 
-0.04831879

Code

cor.test(d_with_pca@sam_data$Dim.2, d_with_pca@sam_data$nb_otu, method = "spearman")

Warning in cor.test.default(d_with_pca@sam_data$Dim.2,
d_with_pca@sam_data$nb_otu, : Impossible de calculer la p-value exacte avec des
ex-aequos


    Spearman's rank correlation rho

data:  d_with_pca@sam_data$Dim.2 and d_with_pca@sam_data$nb_otu
S = 21517, p-value = 0.5037
alternative hypothesis: true rho is not equal to 0
sample estimates:
        rho 
-0.09781111

Code

cor.test(d_with_pca@sam_data$Dim.3, d_with_pca@sam_data$nb_otu, method = "spearman")

Warning in cor.test.default(d_with_pca@sam_data$Dim.3,
d_with_pca@sam_data$nb_otu, : Impossible de calculer la p-value exacte avec des
ex-aequos


    Spearman's rank correlation rho

data:  d_with_pca@sam_data$Dim.3 and d_with_pca@sam_data$nb_otu
S = 23684, p-value = 0.1508
alternative hypothesis: true rho is not equal to 0
sample estimates:
      rho 
-0.208378

Code

(ggstatsplot::ggscatterstats(psmelt_samples_pq(d_with_pca), Dim.1, Hill_0, type = "non-parametric") +
    ggstatsplot::ggscatterstats(psmelt_samples_pq(d_with_pca), Dim.1, Hill_1, type = "non-parametric") +
    ggstatsplot::ggscatterstats(psmelt_samples_pq(d_with_pca), Dim.1, Hill_2, type = "non-parametric")) /
  (ggstatsplot::ggscatterstats(psmelt_samples_pq(d_with_pca), Dim.2, Hill_0, type = "non-parametric") +
     ggstatsplot::ggscatterstats(psmelt_samples_pq(d_with_pca), Dim.2, Hill_1, type = "non-parametric") +
     ggstatsplot::ggscatterstats(psmelt_samples_pq(d_with_pca), Dim.2, Hill_2, type = "non-parametric"))

Taxa are now in rows.

Joining with `by = join_by(Sample)`
Taxa are now in rows.
Joining with `by = join_by(Sample)`
Taxa are now in rows.
Joining with `by = join_by(Sample)`
Taxa are now in rows.
Joining with `by = join_by(Sample)`
Taxa are now in rows.
Joining with `by = join_by(Sample)`
Taxa are now in rows.
Joining with `by = join_by(Sample)`
`stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_xsidebin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_ysidebin()` using `bins = 30`. Pick better value with `binwidth`.

Code

dim_df <- as_tibble(d_with_pca@sam_data) |>
  select(c(starts_with("Dim"), "nb_seq", "nb_otu"))

cor_results <- correlation::correlation(dim_df)
summary(cor_results)

# Correlation Matrix (pearson-method)

Parameter |  nb_otu | nb_seq |     Dim.4 |     Dim.3 |    Dim.2
---------------------------------------------------------------
Dim.1     |   -0.12 |  -0.27 |  2.52e-16 |  5.08e-17 | 1.33e-15
Dim.2     |   -0.17 |  -0.15 | -1.82e-16 | -6.65e-17 |         
Dim.3     |   -0.27 |  -0.24 |  6.62e-18 |           |         
Dim.4     |    0.12 |   0.40 |           |           |         
nb_seq    | 0.67*** |        |           |           |         

p-value adjustment method: Holm (1979)

Code

cor_results %>%
  summary(redundant = TRUE) %>%
  plot()

Code

lon_lat_with_pca <- d_with_pca@sam_data[, c("long", "lat")]
lon_lat_with_pca$long <- as.numeric(lon_lat_with_pca$long)
lon_lat_with_pca$lat <- as.numeric(lon_lat_with_pca$lat)
MEM_with_pca <- dbmem(lon_lat_with_pca, MEM.autocor = "non-null")

d_with_pca@sam_data$MEM_1 <- MEM_with_pca[, 1]
d_with_pca@sam_data$MEM_2 <- MEM_with_pca[, 2]

Code

res_ado_spatial_soil_bray <-
  adonis_pq(
    d_with_pca,
    "MEM_1 + MEM_2 + Dim.1 + Dim.2 + Dim.3 + practice + inter_rank + rank + terroir",
    correction_for_sample_size = TRUE,
    by = "terms"
  )
res_ado_spatial_soil_robAit <-
  adonis_pq(
    d_with_pca,
    "MEM_1 + MEM_2 + Dim.1 + Dim.2 + Dim.3 + practice + inter_rank + rank + terroir",
    correction_for_sample_size = TRUE,
    dist_method = "robust.aitchison",
    by = "terms"
  )

res_ado_spatial_soil_bray_rarefy <-
  adonis_pq(
    rarefy_even_depth(d_with_pca, rngseed = 626),
    "MEM_1 + MEM_2 + Dim.1 + Dim.2 + Dim.3 + practice + inter_rank + rank + terroir",
    correction_for_sample_size = FALSE,
    by = "terms"
  )

`set.seed(626)` was used to initialize repeatable random subsampling.

Please record this for your records so others can reproduce.

Try `set.seed(626); .Random.seed` for the full vector

...

977OTUs were removed because they are no longer 
present in any sample after random subsampling

...

Code

res_ado_spatial_soil_robAit_rarefy <-
  adonis_pq(
    rarefy_even_depth(d_with_pca, rngseed = 626),
    "MEM_1 + MEM_2 + Dim.1 + Dim.2 + Dim.3 + practice + inter_rank + rank + terroir",
    correction_for_sample_size = FALSE,
    dist_method = "robust.aitchison",
    by = "terms"
  )

`set.seed(626)` was used to initialize repeatable random subsampling.

Please record this for your records so others can reproduce.

Try `set.seed(626); .Random.seed` for the full vector

...

977OTUs were removed because they are no longer 
present in any sample after random subsampling

...

Code

anova(betadisper(phyloseq::distance(d_rs, method = "bray"), d_rs@sam_data$practice))

Analysis of Variance Table

Response: Distances
          Df  Sum Sq   Mean Sq F value Pr(>F)
Groups     2 0.01743 0.0087135  0.8894 0.4154
Residuals 72 0.70541 0.0097974

Code

anova(betadisper(phyloseq::distance(d_rs, method = "bray"), d_rs@sam_data$terroir))

Analysis of Variance Table

Response: Distances
          Df  Sum Sq  Mean Sq F value Pr(>F)
Groups    13 0.23559 0.018122  1.4572 0.1605
Residuals 61 0.75865 0.012437

Code

res_ado_rarperm <- adonis_rarperm_pq(d_with_pca,
                                     "MEM_1 + MEM_2 + Dim.1 + Dim.2 + Dim.3 + practice + inter_rank + rank + terroir",
                                     nperm = 9,by = "terms"
)


  |                                                        
  |                                                  |   0%

`set.seed(1)` was used to initialize repeatable random subsampling.

Please record this for your records so others can reproduce.

Try `set.seed(1); .Random.seed` for the full vector

...

976OTUs were removed because they are no longer 
present in any sample after random subsampling

...


  |                                                        
  |======                                            |  11%

`set.seed(2)` was used to initialize repeatable random subsampling.

Please record this for your records so others can reproduce.

Try `set.seed(2); .Random.seed` for the full vector

...

977OTUs were removed because they are no longer 
present in any sample after random subsampling

...


  |                                                        
  |===========                                       |  22%

`set.seed(3)` was used to initialize repeatable random subsampling.

Please record this for your records so others can reproduce.

Try `set.seed(3); .Random.seed` for the full vector

...

995OTUs were removed because they are no longer 
present in any sample after random subsampling

...


  |                                                        
  |=================                                 |  33%

`set.seed(4)` was used to initialize repeatable random subsampling.

Please record this for your records so others can reproduce.

Try `set.seed(4); .Random.seed` for the full vector

...

962OTUs were removed because they are no longer 
present in any sample after random subsampling

...


  |                                                        
  |======================                            |  44%

`set.seed(5)` was used to initialize repeatable random subsampling.

Please record this for your records so others can reproduce.

Try `set.seed(5); .Random.seed` for the full vector

...

968OTUs were removed because they are no longer 
present in any sample after random subsampling

...


  |                                                        
  |============================                      |  56%

`set.seed(6)` was used to initialize repeatable random subsampling.

Please record this for your records so others can reproduce.

Try `set.seed(6); .Random.seed` for the full vector

...

974OTUs were removed because they are no longer 
present in any sample after random subsampling

...


  |                                                        
  |=================================                 |  67%

`set.seed(7)` was used to initialize repeatable random subsampling.

Please record this for your records so others can reproduce.

Try `set.seed(7); .Random.seed` for the full vector

...

995OTUs were removed because they are no longer 
present in any sample after random subsampling

...


  |                                                        
  |=======================================           |  78%

`set.seed(8)` was used to initialize repeatable random subsampling.

Please record this for your records so others can reproduce.

Try `set.seed(8); .Random.seed` for the full vector

...

944OTUs were removed because they are no longer 
present in any sample after random subsampling

...


  |                                                        
  |============================================      |  89%

`set.seed(9)` was used to initialize repeatable random subsampling.

Please record this for your records so others can reproduce.

Try `set.seed(9); .Random.seed` for the full vector

...

1001OTUs were removed because they are no longer 
present in any sample after random subsampling

...


  |                                                        
  |==================================================| 100%

Code

res_ado_rarperm

$mean
           Df   SumOfSqs         R2        F      Pr(>F)
MEM_1       1  0.6586334 0.04633691 3.055062 0.001333333
MEM_2       1  1.4390386 0.10124043 6.675006 0.001000000
Dim.1       1  0.8142545 0.05728553 3.776923 0.001000000
Dim.2       1  0.4278388 0.03009967 1.984521 0.015888889
Dim.3       1  0.4583481 0.03224635 2.126066 0.013222222
practice    2  0.4978426 0.03502501 1.154627 0.234444444
inter_rank  3  0.6165704 0.04337764 0.953326 0.555777778
rank        2  0.6479964 0.04558865 1.502875 0.043222222
terroir    12  3.4793666 0.24478383 1.344919 0.008222222
Residual   24  5.1741120 0.36401599       NA          NA
Total      48 14.2140015 1.00000000       NA          NA

$quantile_min
           Df   SumOfSqs         R2         F Pr(>F)
MEM_1       1  0.6523785 0.04593848 3.0223605 0.0010
MEM_2       1  1.4204560 0.10010571 6.5882157 0.0010
Dim.1       1  0.8091985 0.05685414 3.7488377 0.0010
Dim.2       1  0.4214168 0.02967034 1.9584664 0.0116
Dim.3       1  0.4519256 0.03177274 2.0895145 0.0086
practice    2  0.4942001 0.03472898 1.1429674 0.1990
inter_rank  3  0.6107450 0.04301062 0.9423823 0.5252
rank        2  0.6435660 0.04531086 1.4905851 0.0354
terroir    12  3.4577760 0.24377487 1.3375557 0.0044
Residual   24  5.1592009 0.36241364        NA     NA
Total      48 14.1835242 1.00000000        NA     NA

$quantile_max
           Df   SumOfSqs         R2         F Pr(>F)
MEM_1       1  0.6647908 0.04673184 3.0787917 0.0020
MEM_2       1  1.4586788 0.10249900 6.7726762 0.0010
Dim.1       1  0.8197680 0.05759598 3.8106203 0.0010
Dim.2       1  0.4312398 0.03028679 2.0003311 0.0204
Dim.3       1  0.4618227 0.03253546 2.1440655 0.0160
practice    2  0.5028434 0.03540736 1.1690589 0.2584
inter_rank  3  0.6214720 0.04372579 0.9617194 0.5840
rank        2  0.6532270 0.04596882 1.5192717 0.0518
terroir    12  3.4964165 0.24553364 1.3545084 0.0126
Residual   24  5.1912215 0.36512247        NA     NA
Total      48 14.2424276 1.00000000        NA     NA

Code

df <- data.frame(res_ado_spatial_soil_bray)
df$names <-
  factor(
    rownames(df),
    levels = c(
      "sample_size",
      "MEM_1",
      "MEM_2",
      "Dim.1",
      "Dim.2",
      "Dim.3",
      "practice",
      "inter_rank",
      "rank",
      "terroir",
      "Residual",
      "Total"
    )
  )
df <- df %>% filter(!names == "Total")
p1 <-
  ggplot(df, aes(y = R2 / Df, x = names, fill = R2 / Df)) +
  geom_bar(stat = "identity") +
  scale_fill_viridis_c() +
  geom_text(
    label = case_when(
      df$Pr..F. < 0.001 ~ "***",
      df$Pr..F. < 0.01 ~ "**",
      df$Pr..F. < 0.05 ~ "*",
      df$Pr..F. > 0.05 ~ "ns"
    ),
    nudge_y = 0.02
  ) +
  ggtitle("Permanova on bray")



df <- data.frame(res_ado_spatial_soil_robAit)
df$names <-
  factor(
    rownames(df),
    levels = c(
      "MEM_1",
      "MEM_2",
      "Dim.1",
      "Dim.2",
      "Dim.3",
      "practice",
      "inter_rank",
      "rank",
      "terroir",
      "Residual",
      "Total"
    )
  )
df <- df %>% filter(!names == "Total")

p2 <-
  ggplot(df, aes(y = R2 / Df, x = names, fill = R2 / Df)) +
  geom_bar(stat = "identity") +
  scale_fill_viridis_c() +
  geom_text(
    label = case_when(
      df$Pr..F. < 0.001 ~ "***",
      df$Pr..F. < 0.01 ~ "**",
      df$Pr..F. < 0.05 ~ "*",
      df$Pr..F. > 0.05 ~ "ns"
    ),
    nudge_y = 0.02
  ) +
  ggtitle("Permanova on robust Aitchison")



df <- data.frame(res_ado_spatial_soil_bray_rarefy)
df$names <-
  factor(
    rownames(df),
    levels = c(
      "MEM_1",
      "MEM_2",
      "Dim.1",
      "Dim.2",
      "Dim.3",
      "practice",
      "inter_rank",
      "rank",
      "terroir",
      "Residual",
      "Total"
    )
  )

df <- df %>% filter(!names == "Total")

p3 <-
  ggplot(df, aes(y = R2 / Df, x = names, fill = R2 / Df)) +
  geom_bar(stat = "identity") +
  scale_fill_viridis_c() +
  geom_text(
    label = case_when(
      df$Pr..F. < 0.001 ~ "***",
      df$Pr..F. < 0.01 ~ "**",
      df$Pr..F. < 0.05 ~ "*",
      df$Pr..F. > 0.05 ~ "ns"
    ),
    nudge_y = 0.02
  ) +
  ggtitle("Permanova on bray rarefied")




df <- data.frame(res_ado_spatial_soil_robAit_rarefy)
df$names <-
  factor(
    rownames(df),
    levels = c(
      "MEM_1",
      "MEM_2",
      "Dim.1",
      "Dim.2",
      "Dim.3",
      "practice",
      "inter_rank",
      "rank",
      "terroir",
      "Residual",
      "Total"
    )
  )
df <- df %>% filter(!names == "Total")

p4 <-
  ggplot(df, aes(y = R2 / Df, x = names, fill = R2 / Df)) +
  geom_bar(stat = "identity") +
  scale_fill_viridis_c() +
  geom_text(
    label = case_when(
      df$Pr..F. < 0.001 ~ "***",
      df$Pr..F. < 0.01 ~ "**",
      df$Pr..F. < 0.05 ~ "*",
      df$Pr..F. > 0.05 ~ "ns"
    ),
    nudge_y = 0.02
  ) +
  ggtitle("Permanova on robust Aitchison rarefied")

(p1 + ylim(c(0, 0.14)) + p2 + ylim(c(0, 0.14))) /
  (p3 + ylim(c(0, 0.14)) + p4 + ylim(c(0, 0.14)))

Warning: Removed 1 row containing missing values or values outside the scale range
(`geom_text()`).
Removed 1 row containing missing values or values outside the scale range
(`geom_text()`).
Removed 1 row containing missing values or values outside the scale range
(`geom_text()`).
Removed 1 row containing missing values or values outside the scale range
(`geom_text()`).

Code

# TABLE 2
kbl(res_ado_spatial_bray) |>
  kable_classic(full_width = F, html_font = "Cambria")

	Df	SumOfSqs	R2	F	Pr(>F)
sample_size	1	0.7693445	0.0353335	3.461381	0.001
MEM_1	1	1.5058443	0.0691585	6.774989	0.001
MEM_2	1	1.1000084	0.0505198	4.949081	0.001
practice	2	0.8159499	0.0374739	1.835532	0.007
inter_rank	3	0.7350956	0.0337605	1.102430	0.272
rank	2	0.6418526	0.0294782	1.443889	0.049
terroir	13	4.8702019	0.2236724	1.685513	0.001
Residual	51	11.3355247	0.5206034	NA	NA
Total	74	21.7738218	1.0000000	NA	NA

Code

kbl(res_ado_spatial_soil_bray) |>
  kable_classic(full_width = F, html_font = "Cambria")

	Df	SumOfSqs	R2	F	Pr(>F)
sample_size	1	0.7901843	0.0542150	3.6103129	0.001
MEM_1	1	0.5124004	0.0351561	2.3411323	0.002
MEM_2	1	1.3638777	0.0935764	6.2314898	0.001
Dim.1	1	0.7791081	0.0534550	3.5597062	0.001
Dim.2	1	0.3574619	0.0245256	1.6332256	0.043
Dim.3	1	0.4630058	0.0317671	2.1154506	0.006
practice	2	0.4521423	0.0310217	1.0329080	0.397
inter_rank	3	0.6361246	0.0436448	0.9688073	0.523
rank	2	0.6340566	0.0435030	1.4484867	0.041
terroir	12	3.5526823	0.2437514	1.3526691	0.005
Residual	23	5.0339787	0.3453839	NA	NA
Total	48	14.5750227	1.0000000	NA	NA

Code

# TABLE S4
kbl(res_ado_spatial_bray_rarefy) |>
  kable_classic(full_width = F, html_font = "Cambria")

	Df	SumOfSqs	R2	F	Pr(>F)
MEM_1	1	1.5376111	0.0719461	6.9592951	0.001
MEM_2	1	1.3524271	0.0632812	6.1211440	0.001
practice	2	0.9429846	0.0441230	2.1339947	0.004
inter_rank	3	0.6179406	0.0289140	0.9322754	0.569
rank	2	0.6278679	0.0293785	1.4208788	0.085
terroir	13	4.8038065	0.2247742	1.6724799	0.001
Residual	52	11.4890628	0.5375830	NA	NA
Total	74	21.3717007	1.0000000	NA	NA

Code

# TABLE S5
kbl(res_ado_spatial_soil_bray_rarefy) |>
  kable_classic(full_width = F, html_font = "Cambria")

	Df	SumOfSqs	R2	F	Pr(>F)
MEM_1	1	0.6563568	0.0461721	3.0477747	0.003
MEM_2	1	1.4407089	0.1013481	6.6898923	0.001
Dim.1	1	0.8154816	0.0573659	3.7866665	0.001
Dim.2	1	0.4319417	0.0303854	2.0057094	0.023
Dim.3	1	0.4627161	0.0325502	2.1486096	0.013
practice	2	0.4993959	0.0351305	1.1594655	0.224
inter_rank	3	0.6181950	0.0434876	0.9568572	0.541
rank	2	0.6458273	0.0454314	1.4994406	0.037
terroir	12	3.4762793	0.2445424	1.3451673	0.008
Residual	24	5.1685456	0.3635865	NA	NA
Total	48	14.2154480	1.0000000	NA	NA

Code

# TABLE S6
kbl(res_ado_spatial_robAit_rarefy) |>
  kable_classic(full_width = F, html_font = "Cambria")

	Df	SumOfSqs	R2	F	Pr(>F)
MEM_1	1	323.4837	0.0222816	1.9625739	0.001
MEM_2	1	292.9957	0.0201816	1.7776033	0.006
practice	2	462.2088	0.0318371	1.4021093	0.002
inter_rank	3	428.8210	0.0295373	0.8672182	0.886
rank	2	459.7097	0.0316649	1.3945282	0.017
terroir	13	3979.7626	0.2741271	1.8573230	0.001
Residual	52	8570.9649	0.5903703	NA	NA
Total	74	14517.9464	1.0000000	NA	NA

Code

# TABLE S7
kbl(res_ado_spatial_soil_robAit_rarefy) |>
  kable_classic(full_width = F, html_font = "Cambria")

	Df	SumOfSqs	R2	F	Pr(>F)
MEM_1	1	391.6463	0.0265742	1.4163293	0.002
MEM_2	1	427.9703	0.0290388	1.5476895	0.002
Dim.1	1	603.3451	0.0409385	2.1819061	0.001
Dim.2	1	373.5791	0.0253483	1.3509923	0.023
Dim.3	1	381.3598	0.0258762	1.3791299	0.010
practice	2	597.3217	0.0405298	1.0800617	0.169
inter_rank	3	723.4742	0.0490895	0.8721115	0.858
rank	2	631.8043	0.0428695	1.1424123	0.100
terroir	12	3970.8241	0.2694303	1.1966569	0.005
Residual	24	6636.5288	0.4503050	NA	NA
Total	48	14737.8537	1.0000000	NA	NA

Variance partitionning

Code

plot_var_part_pq(
  var_par_pq(
    physeq = d_with_pca,
    list_component = list(
      "Spatial" = c("MEM_1", "MEM_2"),
      "Soil" = c("Dim.1", "Dim.2", "Dim.3"),
      "Practice" = c("practice", "inter_rank", "rank"),
      "Terroir" = c("terroir")
    )
  ),
  show_dbrda_signif = TRUE
)

Code

# Figure 6a
res_varpart_rarefy <- var_par_rarperm_pq(
  physeq = d_with_pca,
  list_component = list(
    "Spatial" = c("MEM_1", "MEM_2"),
    "Soil" = c("Dim.1", "Dim.2", "Dim.3"),
    "Practice" = c("practice", "inter_rank", "rank"),
    "Terroir" = c("terroir")
  ),
  nperm = 99,
  dbrda_computation = TRUE
)


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Code

plot_var_part_pq(res_varpart_rarefy, show_quantiles = FALSE, filter_quantile_zero = TRUE, show_dbrda_signif = F,digits = 2)

Code

plot_var_part_pq(res_varpart_rarefy, show_quantiles = TRUE, show_dbrda_signif = TRUE)

Code

# Figure 6b
res_varpart_rarefy_wo_soil <- var_par_rarperm_pq(
  physeq = d_rs,
  list_component = list(
    "Spatial" = c("MEM_1", "MEM_2"),
    "Practice" = c("practice", "inter_rank", "rank"),
    "Terroir" = c("terroir")
  ),
  nperm = 99,
  dbrda_computation = TRUE
)


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Code

plot_var_part_pq(res_varpart_rarefy_wo_soil, show_quantiles = FALSE, filter_quantile_zero = TRUE, show_dbrda_signif = F,digits = 2)

Code

plot_var_part_pq(var_par_pq(
  physeq = d_with_pca,
  list_component = list(
    "Spatial" = c("MEM_1", "MEM_2"),
    "Soil" = c("Dim.1", "Dim.2", "Dim.3"),
    "Practice" = c("practice", "inter_rank", "rank"),
    "Terroir" = c("terroir")
  ),
  dist_method = "robust.aitchison"
))

Code

res_varpart_robAit_rarefy <- var_par_rarperm_pq(
  physeq = d_with_pca,
  list_component = list(
    "Spatial" = c("MEM_1", "MEM_2"),
    "Soil" = c("Dim.1", "Dim.2", "Dim.3"),
    "Practice" = c("practice", "inter_rank", "rank"),
    "Terroir" = c("terroir")
  ),
  dist_method = "robust.aitchison",
  nperm = 99
)


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Code

plot_var_part_pq(res_varpart_robAit_rarefy, filter_quantile_zero = TRUE)

Code

res_varpart_rarefy_wo_soil_Aitchison <- var_par_rarperm_pq(
  physeq = d_rs,
  list_component = list(
    "Spatial" = c("MEM_1", "MEM_2"),
    "Practice" = c("practice", "inter_rank", "rank"),
    "Terroir" = c("terroir")
  ),
  dist_method = "robust.aitchison",
  nperm = 99,
  dbrda_computation = TRUE
)


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Code

plot_var_part_pq(res_varpart_rarefy_wo_soil_Aitchison, show_quantiles = FALSE, filter_quantile_zero = TRUE, show_dbrda_signif = F,digits = 2)

Code

res_varpart_rarefy <- var_par_rarperm_pq(
  physeq = d_with_pca,
  list_component = list(
    "Spatial" = c("MEM_1", "MEM_2"),
    "Soil" = c("Dim.1", "Dim.2", "Dim.3"),
    "Practice" = c("practice", "inter_rank", "rank"),
    "Terroir" = c("terroir")
  ),
  nperm = 99
)


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Code

plot_var_part_pq(res_varpart_rarefy, show_quantiles = FALSE, filter_quantile_zero = TRUE)

Code

plot_var_part_pq(res_varpart_rarefy, show_quantiles = TRUE)

New table for resubmission

Terroir

Code

tab_summary <- d_rs@sam_data |>
  cbind(vegan::renyi(d_rs@otu_table, scales = c(0, 1, 2), hill = TRUE)) |>
  as_tibble() |>
  rename("Hill_0" = "0",
         "Hill_1" = "1",
         "Hill_2" = "2") |>
  group_by(terroir) |>
  summarise(
    "mean" = across(
      c(
        "Myc_freq",
        "Myc_intensity_colonization",
        "Arbuscul_abundance",
        "Hill_0",
        "Hill_1",
        "Hill_2",
        "Coarse_sand",
        "Fine_sand",
        "Coarse_silt",
        "Fine_silt",
        "Total_clay",
        "Total_lime",
        "ph",
        "C",
        "SOM",
        "N",
        "C_N",
        "Cu",
        "P2O5",
        "MgO",
        "K20",
        "Na20",
        "CEC",
        "nb_spores"
      ),
      \(x) mean(x, na.rm =TRUE)
    ),
    "sd" = across(
      c(
        "Myc_freq",
        "Myc_intensity_colonization",
        "Arbuscul_abundance",
        "Hill_0",
        "Hill_1",
        "Hill_2",
        "Coarse_sand",
        "Fine_sand",
        "Coarse_silt",
        "Fine_silt",
        "Total_clay",
        "Total_lime",
        "ph",
        "C",
        "SOM",
        "N",
        "C_N",
        "Cu",
        "P2O5",
        "MgO",
        "K20",
        "Na20",
        "CEC",
        "nb_spores"
      ),
      \(x) sd(x, na.rm =TRUE)
    ),
    "n" = across(c("Myc_freq", "Hill_0", "Fine_sand"), function(x) {
      sum(!is.na(x))
    })
  ) |> 
  unnest(cols = c(mean, sd, n), names_sep = "_") |>
  mutate(across(where(is.numeric), function(x) {round(as.numeric(x), 1)})) |>
  mutate_all(~replace(., is.na(.), NA)) |>
  relocate(any_of(c("terroir", "n_Myc_freq", "mean_Myc_freq", "mean_Myc_intensity_colonization", "mean_Arbuscul_abundance", "n_Hill_0", "mean_Hill_0", "mean_Hill_1", "mean_Hill_2", "n_Fine_sand")))|>
  relocate("mean_nb_spores",.after = mean_Arbuscul_abundance)|>
  relocate("sd_nb_spores",.after = mean_Arbuscul_abundance)

tab_summary_terroir <- tab_summary |>
  gt() |>
  cols_width(
    contains("terroir") ~px(150),
    starts_with("n") ~ px(30),
    where(is.numeric) ~ px(65)
  ) |> 
  cols_merge(c("mean_Myc_freq", "sd_Myc_freq"), pattern = "<< {1}  >> <p style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_nb_spores", "sd_nb_spores"), pattern = "<< {1}  >> <p style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_Myc_intensity_colonization", "sd_Myc_intensity_colonization"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_Arbuscul_abundance", "sd_Arbuscul_abundance"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>")|> 
  cols_merge(c("mean_Hill_0", "sd_Hill_0"), pattern = "<< {1}  >> <p style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_Hill_1", "sd_Hill_1"), pattern = "<< {1}  >> <p style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_Hill_2", "sd_Hill_2"), pattern = "<< {1}  >> <p style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_ph", "sd_ph"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_C", "sd_C"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_SOM", "sd_SOM"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_N", "sd_N"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_C_N", "sd_C_N"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_Cu", "sd_Cu"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_P2O5", "sd_P2O5"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_Coarse_sand", "sd_Coarse_sand"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_Fine_sand", "sd_Fine_sand"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_Coarse_silt", "sd_Coarse_silt"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |> 
  cols_merge(c("mean_Fine_silt", "sd_Fine_silt"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_Total_clay", "sd_Total_clay"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_Total_lime", "sd_Total_lime"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_MgO", "sd_MgO"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_K20", "sd_K20"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_Na20", "sd_Na20"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_CEC", "sd_CEC"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  
  cols_label(
    mean_Myc_freq= "F%",
    mean_Myc_intensity_colonization = "M%",
    mean_Arbuscul_abundance = "A%",
    n_Myc_freq = "n",
    mean_Hill_0 = html("H<sup>0</sup>"),
    mean_Hill_1 =  html("H<sup>1</sup>"),
    mean_Hill_2 =  html("H<sup>2</sup>"),
    n_Hill_0 = "n",
    n_Fine_sand = "n",
    mean_ph = "PH",
    mean_C = "C",
    mean_SOM ="SOM", 
    mean_N = "N",
    mean_C_N = "C/N",
    mean_Cu = "Cu",
    mean_P2O5 = "P2O5",
    mean_Coarse_sand = "Coarse sand",
    mean_Fine_sand = "Fine sand",
    mean_Coarse_silt = "Coarse silt",
    mean_Fine_silt = "Fine silt",
    mean_Total_clay = "Clay",
    mean_Total_lime = "Lime",
    mean_MgO = "MgO",
    mean_K20 = "K20",
    mean_Na20 = "Na20",
    mean_CEC = "CEC",
    mean_nb_spores = "Spores"
  )|>
  tab_spanner(
    label = "Soil characteristics",
    columns = -c(terroir, n_Myc_freq, mean_Myc_freq, mean_Myc_intensity_colonization, mean_Arbuscul_abundance, mean_nb_spores, n_Hill_0, mean_Hill_0, mean_Hill_1, mean_Hill_2),
  )   |>
  tab_spanner(
    label = "Fungal colonisation",
    columns = c(n_Myc_freq, mean_Myc_freq, mean_Myc_intensity_colonization, mean_Arbuscul_abundance, mean_nb_spores)
  ) |>
  tab_spanner(
    label = "Hill number",
    columns = c(n_Hill_0, mean_Hill_0, mean_Hill_1, mean_Hill_2),
  )|>
  opt_stylize(style = 1, color = "gray") |>
  tab_style(
    cell_borders(sides = c("left"), color = "gray", weight= px(1)),
    cells_body(columns=c(n_Myc_freq, n_Hill_0, n_Fine_sand)
    ))|>
  tab_style(
    cell_borders(sides = c("left"), color = "gray", weight= px(1)),
    cells_column_labels(columns=c(n_Myc_freq, n_Hill_0, n_Fine_sand)
    ))

gtsave(tab_summary_terroir, "tab_summary_terroir.png", vwidth=2200,expand=20)

Code

tab_summary |>
  gt() |>
  cols_width(
    contains("terroir") ~px(150),
    starts_with("n") ~ px(30),
    where(is.numeric) ~ px(65)
  )|>
  cols_label(
    mean_Myc_freq= "F%",
    mean_Myc_intensity_colonization = "M%",
    mean_Arbuscul_abundance = "A%",
    n_Myc_freq = "n",
    mean_Hill_0 = html("H<sup>0</sup>"),
    mean_Hill_1 =  html("H<sup>1</sup>"),
    mean_Hill_2 =  html("H<sup>2</sup>"),
    n_Hill_0 = "n",
    n_Fine_sand = "n",
    mean_ph = "PH",
    mean_C = "C",
    mean_SOM ="SOM", 
    mean_N = "N",
    mean_C_N = "C/N",
    mean_Cu = "Cu",
    mean_P2O5 = "P2O5",
    mean_Coarse_sand = "Coarse sand",
    mean_Fine_sand = "Fine sand",
    mean_Coarse_silt = "Coarse silt",
    mean_Fine_silt = "Fine silt",
    mean_Total_clay = "Clay",
    mean_Total_lime = "Lime",
    mean_MgO = "MgO",
    mean_K20 = "K20",
    mean_Na20 = "Na20",
    mean_CEC = "CEC",
    mean_nb_spores = "Spores"
  )|>
  tab_spanner(
    label = "Soil characteristics",
    columns = -c(terroir, n_Myc_freq, mean_Myc_freq, mean_Myc_intensity_colonization, mean_Arbuscul_abundance, n_Hill_0, mean_Hill_0, mean_Hill_1, mean_Hill_2),
  )   |>
  tab_spanner(
    label = "Fungal colonisation",
    columns = c(n_Myc_freq, mean_Myc_freq, mean_Myc_intensity_colonization, mean_Arbuscul_abundance, mean_nb_spores),
  ) |>
  tab_spanner(
    label = "Hill number",
    columns = c(n_Hill_0, mean_Hill_0, mean_Hill_1, mean_Hill_2),
  )|>
  opt_stylize(style = 1, color = "gray") |>
  tab_style(
    cell_borders(sides = c("left"), color = "gray", weight= px(1)),
    cells_body(columns=c(n_Myc_freq, n_Hill_0, n_Fine_sand)
    ))|>
  tab_style(
    cell_borders(sides = c("left"), color = "gray", weight= px(1)),
    cells_column_labels(columns=c(n_Myc_freq, n_Hill_0, n_Fine_sand)
    ))

	Fungal colonisation					Fungal colonisation
terroir	n	F%	M%	A%	Soil characteristics																																											Hill number
terroir	n	F%	M%	A%	sd_nb_spores	Spores	n	Coarse sand	Fine sand	Coarse silt	Fine silt	Clay	Lime	PH	C	SOM	N	C/N	Cu	P2O5	MgO	K20	Na20	CEC	sd_Myc_freq	sd_Myc_intensity_colonization	sd_Arbuscul_abundance	sd_Hill_0	sd_Hill_1	sd_Hill_2	sd_Coarse_sand	sd_Fine_sand	sd_Coarse_silt	sd_Fine_silt	sd_Total_clay	sd_Total_lime	sd_ph	sd_C	sd_SOM	sd_N	sd_C_N	sd_Cu	sd_P2O5	sd_MgO	sd_K20	sd_Na20	sd_CEC	n	H⁰	H¹	H²
Aiguesmortes	2	97.9	11.7	8.7	405.9	1286.9	2	541.0	410.0	11.5	13.0	24.5	180.0	8.4	5.4	9.3	0.6	9.8	71.1	328.5	84.3	95.1	11.2	2.2	2.9	3.2	2.2	31.1	9.3	4.3	7.1	12.7	2.1	2.8	0.7	42.4	0.0	0.6	1.0	0.0	1.9	2.5	38.9	1.3	2.0	6.6	0.1	2	242.0	34.9	17.8
Cevennes	4	100.0	17.2	11.0	3280.5	9267.9	4	86.5	111.5	183.8	306.0	312.2	204.0	8.2	11.1	19.0	1.4	8.2	15.7	174.2	274.1	329.2	15.3	16.3	0.0	9.2	7.7	112.0	6.5	3.2	28.9	26.3	31.6	31.2	27.7	93.6	0.1	2.0	3.4	0.1	1.3	4.7	131.6	16.2	130.4	2.0	4.1	4	230.5	17.9	8.7
Côte des Bar	4	97.4	16.9	10.4	1200.4	3620.6	4	148.2	40.8	158.5	304.0	348.5	416.8	8.1	20.6	35.5	2.0	10.4	46.0	242.8	316.2	655.7	14.3	16.3	3.1	3.3	3.9	37.6	4.5	4.0	32.7	12.8	73.2	37.1	78.2	197.3	0.0	3.6	6.1	0.4	0.4	35.4	47.1	93.9	212.5	2.4	4.2	4	96.8	19.1	9.8
Côte des Blancs	15	77.3	17.8	11.7	5101.8	14168.3	0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	6.1	9.3	7.1	15.1	4.0	2.6	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	15	30.5	10.3	6.8
Faugeres	4	99.5	26.7	15.6	4406.3	5885.5	4	297.2	206.2	113.0	175.8	207.8	29.0	7.1	13.2	22.6	1.5	8.6	16.5	80.0	352.0	130.3	33.0	10.2	0.9	10.6	9.2	23.4	7.1	3.6	35.1	52.3	16.7	29.5	39.5	20.5	0.5	2.0	3.5	0.1	0.8	4.7	61.5	141.4	63.6	10.8	1.8	4	204.2	25.1	12.8
Gigondas	4	100.0	21.1	17.9	3388.2	12313.7	4	74.0	84.8	122.8	369.2	349.2	118.2	8.3	19.6	33.6	1.6	12.1	30.4	97.5	309.2	432.0	13.3	18.4	0.0	13.1	11.4	121.4	15.5	5.4	53.6	27.2	68.8	77.3	90.8	66.9	0.1	3.5	6.0	0.3	0.4	12.8	37.3	66.2	115.9	4.4	4.6	4	281.5	44.8	18.3
Langoiran	5	89.6	8.1	5.2	1071.1	3372.7	5	233.2	103.2	309.2	165.2	189.2	0.0	6.4	12.2	20.9	1.2	9.9	57.7	174.2	210.9	280.4	17.2	9.0	7.1	3.2	2.8	63.3	15.9	6.4	60.7	20.4	39.2	20.6	11.5	0.0	0.5	3.8	6.6	0.3	1.1	23.7	57.8	71.0	94.3	2.8	1.6	5	91.8	20.3	8.9
Mercurey	4	85.9	15.2	7.9	711.7	4648.6	4	109.2	52.5	172.2	256.2	409.8	246.5	8.1	20.4	35.1	1.9	10.7	78.3	482.5	315.5	890.8	13.3	20.1	12.3	11.6	6.9	15.5	5.0	3.4	24.0	8.5	47.6	15.4	77.8	149.2	0.1	3.6	6.1	0.2	0.8	27.0	354.5	30.7	229.3	2.2	3.5	4	106.0	22.2	10.3
Montcalm	2	96.9	5.5	3.0	111.0	1318.9	2	571.5	336.5	9.0	36.0	47.0	187.5	8.6	5.8	9.9	0.6	9.1	49.2	308.5	127.1	158.5	9.5	2.4	4.4	0.4	0.5	108.9	18.1	11.1	65.8	92.6	5.7	19.8	12.7	10.6	0.0	1.8	3.0	0.2	0.4	10.5	71.4	41.6	7.0	2.4	0.4	2	107.0	23.3	14.7
Rian	3	87.5	8.6	5.8	468.4	3822.3	3	308.3	137.3	156.7	147.3	250.3	30.0	7.2	14.4	24.7	1.3	10.8	46.7	134.0	243.5	372.7	20.1	12.2	4.2	1.3	1.4	90.3	17.5	9.0	46.5	24.4	66.2	8.6	125.0	30.0	1.2	1.4	2.4	0.2	0.7	20.3	42.2	4.7	213.3	1.4	5.2	3	137.3	21.9	9.3
Tain	4	94.8	11.4	7.9	2170.9	6199.8	4	482.8	135.2	88.2	148.8	145.0	15.0	7.1	10.9	18.8	1.1	10.2	48.6	171.0	175.5	292.3	7.6	7.9	5.5	3.3	4.0	49.7	11.6	6.5	97.5	42.1	9.1	44.6	76.0	17.3	0.5	1.9	3.2	0.2	1.7	27.1	46.1	41.7	37.1	4.0	1.8	4	88.8	22.3	12.8
Vallee de la Marne	4	95.3	26.2	21.1	1489.1	2603.1	4	103.2	146.5	207.2	216.0	327.0	217.2	8.0	27.0	46.5	2.1	12.4	71.9	548.5	548.4	787.5	14.8	18.0	5.5	17.0	14.9	44.9	4.2	1.2	26.0	101.7	59.4	38.3	37.7	76.8	0.2	9.9	17.0	0.7	1.0	66.4	252.8	248.1	375.4	3.9	4.8	4	76.5	16.1	7.9
Vergèze	15	91.2	15.9	10.2	3946.4	6135.2	5	73.6	150.8	239.2	312.8	223.0	150.0	8.2	11.7	20.2	1.1	10.2	35.9	154.6	337.1	468.5	25.7	16.2	12.7	8.9	8.0	26.4	8.1	4.6	26.0	24.7	28.1	16.3	58.8	92.9	0.1	2.1	3.7	0.2	0.5	14.3	51.1	97.0	167.6	3.7	2.2	16	56.2	19.8	11.4
Vosne	4	99.0	14.9	11.2	1991.7	5071.7	4	73.8	92.5	199.2	242.8	391.8	146.2	7.9	23.3	40.1	2.1	10.9	82.6	693.8	368.6	996.9	15.7	21.6	1.2	8.8	8.3	24.9	3.8	2.5	32.2	9.4	28.6	18.0	46.1	45.5	0.1	3.5	6.1	0.3	0.2	17.8	223.2	37.2	262.4	11.9	3.2	4	117.2	21.2	10.3

Practice

Code

tab_summary_p <- d_rs@sam_data |>
  cbind(vegan::renyi(d_rs@otu_table, scales = c(0, 1, 2), hill = TRUE)) |>
  as_tibble() |>
  rename("Hill_0" = "0",
         "Hill_1" = "1",
         "Hill_2" = "2") |>
  group_by(practice) |>
  summarise(
    "mean" = across(
      c(
        "Myc_freq",
        "Myc_intensity_colonization",
        "Arbuscul_abundance",
        "Hill_0",
        "Hill_1",
        "Hill_2",
        "Coarse_sand",
        "Fine_sand",
        "Coarse_silt",
        "Fine_silt",
        "Total_clay",
        "Total_lime",
        "ph",
        "C",
        "SOM",
        "N",
        "C_N",
        "Cu",
        "P2O5",
        "MgO",
        "K20",
        "Na20",
        "CEC",
        "nb_spores"
      ),
      \(x) mean(x, na.rm =TRUE)
    ),
    "sd" = across(
      c(
        "Myc_freq",
        "Myc_intensity_colonization",
        "Arbuscul_abundance",
        "Hill_0",
        "Hill_1",
        "Hill_2",
        "Coarse_sand",
        "Fine_sand",
        "Coarse_silt",
        "Fine_silt",
        "Total_clay",
        "Total_lime",
        "ph",
        "C",
        "SOM",
        "N",
        "C_N",
        "Cu",
        "P2O5",
        "MgO",
        "K20",
        "Na20",
        "CEC",
        "nb_spores"
      ),
      \(x) sd(x, na.rm =TRUE)
    ),
    "n" = across(c("Myc_freq", "Hill_0", "Fine_sand"), function(x) {
      sum(!is.na(x))
    })
  ) |> 
  unnest(cols = c(mean, sd, n), names_sep = "_") |>
  mutate(across(where(is.numeric), function(x) {round(as.numeric(x), 1)})) |>
  mutate_all(~replace(., is.na(.), NA)) |>
  relocate(any_of(c("practice", "n_Myc_freq", "mean_Myc_freq", "mean_Myc_intensity_colonization", "mean_Arbuscul_abundance", "n_Hill_0", "mean_Hill_0", "mean_Hill_1", "mean_Hill_2", "n_Fine_sand")))|>
  relocate("mean_nb_spores",.after = mean_Arbuscul_abundance)|>
  relocate("sd_nb_spores",.after = mean_Arbuscul_abundance)

tab_summary_practice <- tab_summary_p |>
  gt() |>
  cols_width(
    contains("practice") ~px(150),
    starts_with("n") ~ px(30),
    where(is.numeric) ~ px(65)
  ) |> 
  cols_merge(c("mean_Myc_freq", "sd_Myc_freq"), pattern = "<< {1}  >> <p style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_nb_spores", "sd_nb_spores"), pattern = "<< {1}  >> <p style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_Myc_intensity_colonization", "sd_Myc_intensity_colonization"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_Arbuscul_abundance", "sd_Arbuscul_abundance"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>")|> 
  cols_merge(c("mean_Hill_0", "sd_Hill_0"), pattern = "<< {1}  >> <p style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_Hill_1", "sd_Hill_1"), pattern = "<< {1}  >> <p style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_Hill_2", "sd_Hill_2"), pattern = "<< {1}  >> <p style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_ph", "sd_ph"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_C", "sd_C"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_SOM", "sd_SOM"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_N", "sd_N"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_C_N", "sd_C_N"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_Cu", "sd_Cu"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_P2O5", "sd_P2O5"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_Coarse_sand", "sd_Coarse_sand"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_Fine_sand", "sd_Fine_sand"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_Coarse_silt", "sd_Coarse_silt"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |> 
  cols_merge(c("mean_Fine_silt", "sd_Fine_silt"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_Total_clay", "sd_Total_clay"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_Total_lime", "sd_Total_lime"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_MgO", "sd_MgO"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_K20", "sd_K20"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_Na20", "sd_Na20"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  cols_merge(c("mean_CEC", "sd_CEC"), pattern = "<< {1}  >> <p  style='font-size:0.7em'>  << ± {2}>></span> >>") |>
  
  cols_label(
    mean_Myc_freq= "F%",
    mean_Myc_intensity_colonization = "M%",
    mean_Arbuscul_abundance = "A%",
    n_Myc_freq = "n",
    mean_Hill_0 = html("H<sup>0</sup>"),
    mean_Hill_1 =  html("H<sup>1</sup>"),
    mean_Hill_2 =  html("H<sup>2</sup>"),
    n_Hill_0 = "n",
    n_Fine_sand = "n",
    mean_ph = "PH",
    mean_C = "C",
    mean_SOM ="SOM", 
    mean_N = "N",
    mean_C_N = "C/N",
    mean_Cu = "Cu",
    mean_P2O5 = "P2O5",
    mean_Coarse_sand = "Coarse sand",
    mean_Fine_sand = "Fine sand",
    mean_Coarse_silt = "Coarse silt",
    mean_Fine_silt = "Fine silt",
    mean_Total_clay = "Clay",
    mean_Total_lime = "Lime",
    mean_MgO = "MgO",
    mean_K20 = "K20",
    mean_Na20 = "Na20",
    mean_CEC = "CEC",
    mean_nb_spores = "Spores"
  )|>
  tab_spanner(
    label = "Soil characteristics",
    columns = -c(practice, n_Myc_freq, mean_Myc_freq, mean_Myc_intensity_colonization, mean_Arbuscul_abundance, mean_nb_spores, n_Hill_0, mean_Hill_0, mean_Hill_1, mean_Hill_2),
  )   |>
  tab_spanner(
    label = "Fungal colonisation",
    columns = c(n_Myc_freq, mean_Myc_freq, mean_Myc_intensity_colonization, mean_Arbuscul_abundance, mean_nb_spores)
  ) |>
  tab_spanner(
    label = "Hill number",
    columns = c(n_Hill_0, mean_Hill_0, mean_Hill_1, mean_Hill_2),
  )|>
  opt_stylize(style = 1, color = "gray") |>
  tab_style(
    cell_borders(sides = c("left"), color = "gray", weight= px(1)),
    cells_body(columns=c(n_Myc_freq, n_Hill_0, n_Fine_sand)
    ))|>
  tab_style(
    cell_borders(sides = c("left"), color = "gray", weight= px(1)),
    cells_column_labels(columns=c(n_Myc_freq, n_Hill_0, n_Fine_sand)
    ))

gtsave(tab_summary_practice, "tab_summary_practice.png", vwidth=2200,expand=20)


tab_summary_p |>
  gt() |>
  cols_width(
    contains("practice") ~px(150),
    starts_with("n") ~ px(30),
    where(is.numeric) ~ px(65)
  ) |> 
  cols_label(
    mean_Myc_freq= "F%",
    mean_Myc_intensity_colonization = "M%",
    mean_Arbuscul_abundance = "A%",
    n_Myc_freq = "n",
    mean_Hill_0 = html("H<sup>0</sup>"),
    mean_Hill_1 =  html("H<sup>1</sup>"),
    mean_Hill_2 =  html("H<sup>2</sup>"),
    n_Hill_0 = "n",
    n_Fine_sand = "n",
    mean_ph = "PH",
    mean_C = "C",
    mean_SOM ="SOM", 
    mean_N = "N",
    mean_C_N = "C/N",
    mean_Cu = "Cu",
    mean_P2O5 = "P2O5",
    mean_Coarse_sand = "Coarse sand",
    mean_Fine_sand = "Fine sand",
    mean_Coarse_silt = "Coarse silt",
    mean_Fine_silt = "Fine silt",
    mean_Total_clay = "Clay",
    mean_Total_lime = "Lime",
    mean_MgO = "MgO",
    mean_K20 = "K20",
    mean_Na20 = "Na20",
    mean_CEC = "CEC"
  )|>
  tab_spanner(
    label = "Soil characteristics",
    columns = -c(practice, n_Myc_freq, mean_Myc_freq, mean_Myc_intensity_colonization, mean_Arbuscul_abundance, n_Hill_0, mean_Hill_0, mean_Hill_1, mean_Hill_2),
  )   |>
  tab_spanner(
    label = "Fungal colonisation",
    columns = c(n_Myc_freq, mean_Myc_freq, mean_Myc_intensity_colonization, mean_Arbuscul_abundance),
  ) |>
  tab_spanner(
    label = "Hill number",
    columns = c(n_Hill_0, mean_Hill_0, mean_Hill_1, mean_Hill_2),
  )|>
  opt_stylize(style = 1, color = "gray") |>
  tab_style(
    cell_borders(sides = c("left"), color = "gray", weight= px(1)),
    cells_body(columns=c(n_Myc_freq, n_Hill_0, n_Fine_sand)
    ))|>
  tab_style(
    cell_borders(sides = c("left"), color = "gray", weight= px(1)),
    cells_column_labels(columns=c(n_Myc_freq, n_Hill_0, n_Fine_sand)
    ))

practice	Fungal colonisation				Soil characteristics																																											Hill number
practice	n	F%	M%	A%	sd_nb_spores	mean_nb_spores	n	Coarse sand	Fine sand	Coarse silt	Fine silt	Clay	Lime	PH	C	SOM	N	C/N	Cu	P2O5	MgO	K20	Na20	CEC	sd_Myc_freq	sd_Myc_intensity_colonization	sd_Arbuscul_abundance	sd_Hill_0	sd_Hill_1	sd_Hill_2	sd_Coarse_sand	sd_Fine_sand	sd_Coarse_silt	sd_Fine_silt	sd_Total_clay	sd_Total_lime	sd_ph	sd_C	sd_SOM	sd_N	sd_C_N	sd_Cu	sd_P2O5	sd_MgO	sd_K20	sd_Na20	sd_CEC	n	H⁰	H¹	H²
Conventional	19	87.6	10.6	6.1	4336.5	5455.3	14	252.7	149.3	157.4	198.4	242.3	136.2	7.7	13.1	22.5	1.3	10.0	44.1	217.9	235.2	334.1	15.5	11.5	11.8	6.0	3.5	107.6	15.4	5.9	174.7	107.5	99.0	103.8	112.5	142.7	0.9	5.5	9.5	0.4	1.5	29.3	141.2	78.6	199.6	8.2	5.9	19	96.6	17.9	8.9
Conversion	17	88.9	12.6	8.4	5356.3	7036.4	12	247.2	158.2	146.8	186.2	261.6	175.5	7.9	14.6	25.1	1.4	10.4	48.5	324.0	293.2	481.4	16.1	13.3	10.1	7.3	5.8	82.5	10.8	5.2	177.1	122.6	89.9	99.2	139.7	170.4	0.6	5.6	9.6	0.5	1.0	26.6	230.4	203.1	308.0	9.3	7.2	17	119.5	21.8	11.8
Organic	38	93.1	20.8	14.5	5568.5	8189.8	23	160.0	116.0	185.6	260.6	277.8	136.3	7.7	17.9	30.7	1.7	10.5	52.7	275.8	335.7	564.1	18.1	15.9	10.1	10.1	9.0	78.5	9.1	4.7	148.1	57.4	75.1	81.5	116.8	123.6	0.7	7.8	13.5	0.6	1.6	36.8	273.5	114.0	366.8	7.9	5.6	39	99.1	20.5	10.7

Code

practice_for_soil_prop <- unlist(lapply(strsplit(rownames(res_pca$tab), "--"), function(x) {
  x[5]
}))

Differential abundance analysis

Indicspecies

Terroir

Code

res_mpt_terroir <-
  multipatt(as.matrix(d_rs@otu_table),
            d_rs@sam_data$terroir,
            control = how(nperm = 9999),
            max.order = 3
  )

res_mpt_terroir_df <- res_mpt_terroir$sign
res_mpt_terroir_df$p.adj <- p.adjust(res_mpt_terroir_df$p.value, method = "BH")
res_mpt_terroir_df$OTU_names <- rownames(res_mpt_terroir_df)
res_mpt_terroir_df_signif <-
  res_mpt_terroir_df %>%
  filter(p.adj < 0.05) %>%
  tidyr::pivot_longer(cols = starts_with("s."))

tax_tab <- as.data.frame(d_rs@tax_table)
tax_tab$otu <- rownames(tax_tab)

res_mpt_terroir_df_signif_taxo <- left_join(res_mpt_terroir_df_signif,tax_tab, by = join_by("OTU_names" == "otu"))

ggplot(
  res_mpt_terroir_df_signif_taxo,
  aes(
    x = OTU_names,
    y = name,
    alpha = value,
    color = Genus_PR2
  )
) +
  geom_point(size = 6) +
  theme(axis.text.x = element_text(
    angle = 90,
    vjust = 0.5,
    hjust = 1
  ))+
  scale_alpha(range = c(0, 1))

Code

res_mpt_terroir_rg <-
  multipatt(as.matrix(rarefy_even_depth(d_rs, rngseed = 626)@otu_table),
            d_rs@sam_data$terroir,
            control = how(nperm = 9999),
            max.order = 3,
            func = "r.g"
  )

`set.seed(626)` was used to initialize repeatable random subsampling.

Please record this for your records so others can reproduce.

Try `set.seed(626); .Random.seed` for the full vector

...

1258OTUs were removed because they are no longer 
present in any sample after random subsampling

...

Code

res_mpt_terroir_rg_df <- res_mpt_terroir_rg$sign
res_mpt_terroir_rg_df$p.adj <- p.adjust(res_mpt_terroir_rg_df$p.value, method = "BH")
res_mpt_terroir_rg_df$OTU_names <- rownames(res_mpt_terroir_rg_df)
res_mpt_terroir_rg_df_signif <-
  res_mpt_terroir_rg_df %>%
  filter(p.adj < 0.05) %>%
  tidyr::pivot_longer(cols = starts_with("s."))

ggplot(
  res_mpt_terroir_rg_df_signif,
  aes(
    x = OTU_names,
    y = name,
    alpha = value,
    color = stat
  )
) +
  geom_point(size = 4) +
  theme(axis.text.x = element_text(
    angle = 90,
    vjust = 0.5,
    hjust = 1
  ))

Code

tapply(d_rs@otu_table[,"OTU_19"] , d_rs@sam_data$terroir, mean)

      Aiguesmortes           Cevennes       Côte des Bar    Côte des Blancs 
           0.00000          189.00000            0.00000           30.53333 
          Faugeres           Gigondas          Langoiran           Mercurey 
        1387.25000          479.25000          271.20000          205.75000 
          Montcalm               Rian               Tain Vallee de la Marne 
         336.00000            0.00000          706.00000           22.00000 
           Vergèze              Vosne 
         466.56250          344.25000

Code

tapply(d_rs@otu_table[,"OTU_19"] , d_rs@sam_data$terroir, function(x) {sum(x>0)})

      Aiguesmortes           Cevennes       Côte des Bar    Côte des Blancs 
                 0                  2                  0                  3 
          Faugeres           Gigondas          Langoiran           Mercurey 
                 2                  4                  3                  2 
          Montcalm               Rian               Tain Vallee de la Marne 
                 1                  0                  3                  1 
           Vergèze              Vosne 
                 5                  2

practice

Code

res_mpt_practice <-
  multipatt(as.matrix(d_rs@otu_table),
            d_rs@sam_data$practice,
            control = how(nperm = 9999),
            max.order = 3
  )

res_mpt_practice_df <- res_mpt_practice$sign
res_mpt_practice_df$p.adj <- p.adjust(res_mpt_practice_df$p.value, method = "BH")
res_mpt_practice_df$OTU_names <- rownames(res_mpt_practice_df)
res_mpt_practice_df_signif <-
  res_mpt_practice_df %>%
  filter(p.adj < 0.05) %>%
  tidyr::pivot_longer(cols = starts_with("s."))

ggplot(
  res_mpt_practice_df_signif,
  aes(
    x = OTU_names,
    y = name,
    size = 2 * value,
    color = stat
  )
) +
  geom_point() +
  theme(axis.text.x = element_text(
    angle = 90,
    vjust = 0.5,
    hjust = 1
  ))

Code

res_mpt_practice_rg <-
  multipatt(as.matrix(d_rs@otu_table),
            d_rs@sam_data$practice,
            control = how(nperm = 9999),
            max.order = 3,
            func = "r.g"
  )

res_mpt_practice_rg_df <- res_mpt_practice_rg$sign
res_mpt_practice_rg_df$p.adj <- p.adjust(res_mpt_practice_rg_df$p.value, method = "BH")
res_mpt_practice_rg_df$OTU_names <- rownames(res_mpt_practice_rg_df)
res_mpt_practice_rg_df_signif <-
  res_mpt_practice_rg_df %>%
  filter(p.adj < 0.05) %>%
  tidyr::pivot_longer(cols = starts_with("s."))

ggplot(
  res_mpt_practice_rg_df_signif,
  aes(
    x = OTU_names,
    y = name,
    size = value,
    color = stat
  )
) +
  geom_point() +
  theme(axis.text.x = element_text(
    angle = 90,
    vjust = 0.5,
    hjust = 1
  ))

Session information

Code

sessionInfo()

R version 4.4.1 (2024-06-14)
Platform: x86_64-pc-linux-gnu
Running under: Debian GNU/Linux 12 (bookworm)

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0

locale:
 [1] LC_CTYPE=fr_FR.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=fr_FR.UTF-8        LC_COLLATE=fr_FR.UTF-8    
 [5] LC_MONETARY=fr_FR.UTF-8    LC_MESSAGES=fr_FR.UTF-8   
 [7] LC_PAPER=fr_FR.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C       

time zone: Europe/Paris
tzcode source: system (glibc)

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] geodist_0.1.0               conflicted_1.2.0           
 [3] gt_0.11.1                   lubridate_1.9.3            
 [5] forcats_1.0.0               stringr_1.5.1              
 [7] readr_2.1.5                 tidyr_1.3.1                
 [9] tibble_3.2.1                tidyverse_2.0.0            
[11] ComplexUpset_1.3.3          microbiomeMarker_1.10.0    
[13] formattable_0.2.1           kableExtra_1.4.0           
[15] adegraphics_1.0-21          ade4_1.7-22                
[17] adespatial_0.3-24           sf_1.0-17                  
[19] indicspecies_1.7.15         DESeq2_1.44.0              
[21] SummarizedExperiment_1.34.0 Biobase_2.64.0             
[23] MatrixGenerics_1.16.0       matrixStats_1.4.1          
[25] GenomicRanges_1.56.1        GenomeInfoDb_1.40.1        
[27] IRanges_2.38.1              S4Vectors_0.42.1           
[29] BiocGenerics_0.50.0         factoextra_1.0.7           
[31] FactoMineR_2.11             vegan_2.6-8                
[33] lattice_0.22-6              permute_0.9-7              
[35] patchwork_1.3.0             knitr_1.48                 
[37] DT_0.33                     metacoder_0.3.7            
[39] MiscMetabar_0.10.1          purrr_1.0.2                
[41] dplyr_1.1.4                 dada2_1.32.0               
[43] Rcpp_1.0.13                 ggplot2_3.5.1              
[45] phyloseq_1.48.0             targets_1.8.0              

loaded via a namespace (and not attached):
  [1] igraph_2.0.3                    mia_1.12.0                     
  [3] Formula_1.2-5                   rematch2_2.1.2                 
  [5] scater_1.32.1                   zlibbioc_1.50.0                
  [7] BWStest_0.2.3                   tidyselect_1.2.1               
  [9] bit_4.5.0                       doParallel_1.0.17              
 [11] clue_0.3-65                     rjson_0.2.23                   
 [13] rngtools_1.5.2                  S4Arrays_1.4.1                 
 [15] parallel_4.4.1                  RNeXML_2.4.11                  
 [17] correlation_0.8.5               png_0.1-8                      
 [19] cli_3.6.3                       ggplotify_0.1.2                
 [21] CVXR_1.0-14                     bayestestR_0.14.0              
 [23] multtest_2.60.0                 MultiAssayExperiment_1.30.3    
 [25] bluster_1.14.0                  BiocNeighbors_1.22.0           
 [27] TreeSummarizedExperiment_2.12.0 adegenet_2.1.10                
 [29] mime_0.12                       evaluate_1.0.0                 
 [31] tidytree_0.4.6                  ComplexHeatmap_2.20.0          
 [33] ggh4x_0.2.8                     stringi_1.8.4                  
 [35] PMCMRplus_1.9.12                backports_1.5.0                
 [37] lmerTest_3.1-3                  gsl_2.1-8                      
 [39] XML_3.99-0.17                   Exact_3.3                      
 [41] ggVennDiagram_1.5.2             httpuv_1.6.15                  
 [43] Wrench_1.22.0                   paletteer_1.6.0                
 [45] magrittr_2.0.3                  leaps_3.2                      
 [47] splines_4.4.1                   RApiSerialize_0.1.4            
 [49] jpeg_0.1-10                     doRNG_1.8.6                    
 [51] wk_0.9.3                        rootSolve_1.8.2.4              
 [53] ggbeeswarm_0.7.2                DBI_1.2.3                      
 [55] withr_3.0.1                     class_7.3-22                   
 [57] systemfonts_1.1.0               SuppDists_1.1-9.8              
 [59] htmlwidgets_1.6.4               fs_1.6.4                       
 [61] SingleCellExperiment_1.26.0     ggrepel_0.9.6                  
 [63] labeling_0.4.3                  SparseArray_1.4.8              
 [65] cellranger_1.1.0                flashClust_1.01-2              
 [67] rncl_0.8.7                      see_0.9.0                      
 [69] lmom_3.2                        effectsize_0.8.9               
 [71] zoo_1.8-12                      XVector_0.44.0                 
 [73] UCSC.utils_1.0.0                timechange_0.3.0               
 [75] decontam_1.24.0                 secretbase_1.0.3               
 [77] foreach_1.5.2                   fansi_1.0.6                    
 [79] caTools_1.18.3                  grid_4.4.1                     
 [81] data.table_1.16.0               ggtree_3.12.0                  
 [83] rhdf5_2.48.0                    pwalign_1.0.0                  
 [85] irlba_2.3.5.1                   gridGraphics_0.5-1             
 [87] DescTools_0.99.57               base64url_1.4                  
 [89] lazyeval_0.2.2                  yaml_2.3.10                    
 [91] survival_3.7-0                  crayon_1.5.3                   
 [93] RColorBrewer_1.1-3              later_1.3.2                    
 [95] codetools_0.2-20                base64enc_0.1-3                
 [97] GlobalOptions_0.1.2             shape_1.4.6.1                  
 [99] limma_3.60.6                    estimability_1.5.1             
[101] Rsamtools_2.20.0                ggside_0.3.1                   
[103] foreign_0.8-87                  pkgconfig_2.0.3                
[105] ggpubr_0.6.0                    xml2_1.3.6                     
[107] GenomicAlignments_1.40.0        aplot_0.2.3                    
[109] scatterplot3d_0.3-44            ape_5.8                        
[111] viridisLite_0.4.2               xtable_1.8-4                   
[113] interp_1.1-6                    car_3.1-3                      
[115] highr_0.11                      hwriter_1.3.2.1                
[117] plyr_1.8.9                      httr_1.4.7                     
[119] rbibutils_2.3                   tools_4.4.1                    
[121] broom_1.0.7                     beeswarm_0.4.0                 
[123] htmlTable_2.4.3                 checkmate_2.3.2                
[125] nlme_3.1-166                    PCAtest_0.0.1                  
[127] lme4_1.1-35.5                   digest_0.6.37                  
[129] numDeriv_2016.8-1.1             adephylo_1.1-16                
[131] Matrix_1.7-0                    farver_2.1.2                   
[133] tzdb_0.4.0                      reshape2_1.4.4                 
[135] yulab.utils_0.1.7               viridis_0.6.5                  
[137] DirichletMultinomial_1.46.0     rpart_4.1.23                   
[139] glue_1.8.0                      cachem_1.1.0                   
[141] Hmisc_5.1-3                     generics_0.1.3                 
[143] Biostrings_2.72.1               classInt_0.4-10                
[145] mvtnorm_1.3-1                   spdep_1.3-6                    
[147] metagenomeSeq_1.46.0            webshot2_0.1.1                 
[149] statmod_1.5.0                   biomformat_1.32.0              
[151] ScaledMatrix_1.12.0             carData_3.0-5                  
[153] minqa_1.2.8                     pbapply_1.7-2                  
[155] ANCOMBC_2.6.0                   glmnet_4.1-8                   
[157] utf8_1.2.4                      seqinr_4.2-36                  
[159] gtools_3.9.5                    readxl_1.4.3                   
[161] datawizard_0.13.0               ggsignif_0.6.4                 
[163] gridExtra_2.3                   shiny_1.9.1                    
[165] GenomeInfoDbData_1.2.12         energy_1.7-12                  
[167] rhdf5filters_1.16.0             parameters_0.22.2              
[169] memoise_2.0.1                   rmarkdown_2.28                 
[171] scales_1.3.0                    gld_2.6.6                      
[173] chromote_0.3.1                  svglite_2.1.3                  
[175] phylobase_0.8.12                stringfish_0.16.0              
[177] rstudioapi_0.16.0               cluster_2.1.6                  
[179] hms_1.1.3                       ggcorrplot_0.1.4.1             
[181] munsell_0.5.1                   colorspace_2.1-1               
[183] rlang_1.1.4                     s2_1.1.7                       
[185] DelayedMatrixStats_1.26.0       sparseMatrixStats_1.16.0       
[187] circlize_0.4.16                 scuttle_1.14.0                 
[189] mgcv_1.9-1                      xfun_0.48                      
[191] multcompView_0.1-10             coda_0.19-4.1                  
[193] e1071_1.7-16                    TH.data_1.1-2                  
[195] plotROC_2.3.1                   iterators_1.0.14               
[197] emmeans_1.10.4                  abind_1.4-8                    
[199] treeio_1.28.0                   gmp_0.7-5                      
[201] Rhdf5lib_1.26.0                 DECIPHER_3.0.0                 
[203] bitops_1.0-9                    Rdpack_2.6.1                   
[205] ps_1.8.0                        promises_1.3.0                 
[207] sandwich_3.1-1                  DelayedArray_0.30.1            
[209] proxy_0.4-27                    Rmpfr_0.9-5                    
[211] compiler_4.4.1                  statsExpressions_1.6.0         
[213] prettyunits_1.2.0               boot_1.3-31                    
[215] beachmat_2.20.0                 BiocSingular_1.20.0            
[217] kSamples_1.2-10                 qs_0.27.2                      
[219] units_0.8-5                     MASS_7.3-61                    
[221] progress_1.2.3                  uuid_1.2-1                     
[223] BiocParallel_1.38.0             insight_0.20.5                 
[225] R6_2.5.1                        rstatix_0.7.2                  
[227] fastmap_1.2.0                   multcomp_1.4-26                
[229] vipor_0.4.7                     websocket_1.4.2                
[231] rsvd_1.0.5                      ggstatsplot_0.12.4             
[233] nnet_7.3-19                     gtable_0.3.5                   
[235] KernSmooth_2.23-24              latticeExtra_0.6-30            
[237] deldir_2.0-4                    htmltools_0.5.8.1              
[239] RcppParallel_5.1.9              bit64_4.5.2                    
[241] lifecycle_1.0.4                 processx_3.8.4                 
[243] spData_2.3.3                    nloptr_2.1.1                   
[245] callr_3.7.6                     sass_0.4.9                     
[247] vctrs_0.6.5                     zeallot_0.1.0                  
[249] ggfun_0.1.6                     sp_2.1-4                       
[251] pillar_1.9.0                    prismatic_1.1.2                
[253] gplots_3.2.0                    ShortRead_1.62.0               
[255] locfit_1.5-9.10                 jsonlite_1.8.9                 
[257] expm_1.0-0                      GetoptLong_1.0.5